install required packages (only need to do this once?)

install.packages(“ggplot2”); # for graphics functions install.packages(“car”); # for the leveneTest() function install.packages(“pastecs”); # for the stat.desc() function install.packages(“psych”); # for the describe() function install.packages(“hrbrthemes”) install.packages(“viridis”)

“call” the required packages (need to do this every session?)

library(car); library(ggplot2); library(pastecs); library(psych); library(hrbrthemes); library(viridis)
Loading required package: carData
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

Attaching package: ‘psych’

The following objects are masked from ‘package:ggplot2’:

    %+%, alpha

The following object is masked from ‘package:car’:

    logit

Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio
NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
      Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
      if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow
Loading required package: viridisLite

Use Excel to generate a .csv file with “tidy” data (each row = 1 case / subject, 1st row is column names). Import .CSV file into R “dataframe” called “watermazedata”. Then show the “watermazedata” dataframe (header + 1st 8 data rows) to check it out

watermazedata <- read.csv(file="./data_clean/water maze all.csv", header=TRUE, sep=",")
watermazedata

Derive new variables. Mostly use the rowMeans() function, but these may be useful as well…

Make some new DVs (~“columns”) assigned 1 (TRUE) or 0 (FALSE) based on Boolean calculations: - Less than? watermazedata\(Duration.Spatial2LessThanSpatial1 <- watermazedata\)Duration.Spatial2 < watermazedata\(Duration.Spatial2 - Less than or equal to? watermazedata\)Duration.Spatial1LessThanOrEqualTo60 <- watermazedata\(Duration.Spatial2 <= 60 - Equal to? watermazedata\)Sh <- watermazedata\(Treatment == "Sh" - Not equal to? watermazedata\)NotSh <- watermazedata$Treatment != “Sh”

Use “ifelse” to maybe replace scores (e.g., replace any duration greater than 60 with NA “missing data”, else keep the same) - watermazedata\(Duration.Spatial.Clean <- ifelse(watermazedata\)Duration.Spatial > 60, NA, watermazedata$Duration.Spatial)

# Trials 1-10 for cued, spatial 1, spatial 2 and spatial trials averaged into 5 blocks (2 trials each) each for both Distance and Duration
watermazedata$Duration.Cued.Block1 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.1,
                                                 watermazedata$Duration.Cued.2),
                                               na.rm = TRUE)
watermazedata$Duration.Cued.Block2 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.3,
                                                 watermazedata$Duration.Cued.4),
                                               na.rm = TRUE)
watermazedata$Duration.Cued.Block3 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.5,
                                                 watermazedata$Duration.Cued.6),
                                               na.rm = TRUE)
watermazedata$Duration.Cued.Block4 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.7,
                                                 watermazedata$Duration.Cued.8),
                                               na.rm = TRUE)
watermazedata$Duration.Cued.Block5 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.9,
                                                 watermazedata$Duration.Cued.10),
                                               na.rm = TRUE)

watermazedata$Duration.Spatial1.Block1 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.1,
                                                     watermazedata$Duration.Spatial1.2),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial1.Block2 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.3,
                                                     watermazedata$Duration.Spatial1.4),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial1.Block3 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.5,
                                                     watermazedata$Duration.Spatial1.6),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial1.Block4 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.7,
                                                     watermazedata$Duration.Spatial1.8),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial1.Block5 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.9,
                                                     watermazedata$Duration.Spatial1.10),
                                                   na.rm = TRUE)

watermazedata$Duration.Spatial2.Block1 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.1,
                                                     watermazedata$Duration.Spatial2.2),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial2.Block2 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.3,
                                                     watermazedata$Duration.Spatial2.4),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial2.Block3 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.5,
                                                     watermazedata$Duration.Spatial2.6),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial2.Block4 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.7,
                                                     watermazedata$Duration.Spatial2.8),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial2.Block5 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.9,
                                                     watermazedata$Duration.Spatial2.10),
                                                   na.rm = TRUE)

watermazedata$Duration.Spatial3.Block1 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.1,
                                                     watermazedata$Duration.Spatial3.2),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial3.Block2 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.3,
                                                     watermazedata$Duration.Spatial3.4),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial3.Block3 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.5,
                                                     watermazedata$Duration.Spatial3.6),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial3.Block4 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.7,
                                                     watermazedata$Duration.Spatial3.8),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial3.Block5 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.9,
                                                     watermazedata$Duration.Spatial3.10),
                                                   na.rm = TRUE)

watermazedata$Distance.Cued.Block1 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.1,
                                                 watermazedata$Distance.Cued.2),
                                               na.rm = TRUE)
watermazedata$Distance.Cued.Block2 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.3,
                                                 watermazedata$Distance.Cued.4),
                                               na.rm = TRUE)
watermazedata$Distance.Cued.Block3 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.5,
                                                 watermazedata$Distance.Cued.6),
                                               na.rm = TRUE)
watermazedata$Distance.Cued.Block4 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.7,
                                                 watermazedata$Distance.Cued.8),
                                               na.rm = TRUE)
watermazedata$Distance.Cued.Block5 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.9,
                                                 watermazedata$Distance.Cued.10),
                                               na.rm = TRUE)

watermazedata$Distance.Spatial1.Block1 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.1,
                                                     watermazedata$Distance.Spatial1.2),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial1.Block2 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.3,
                                                     watermazedata$Distance.Spatial1.4),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial1.Block3 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.5,
                                                     watermazedata$Distance.Spatial1.6),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial1.Block4 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.7,
                                                     watermazedata$Distance.Spatial1.8),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial1.Block5 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.9,
                                                     watermazedata$Distance.Spatial1.10),
                                                   na.rm = TRUE)

watermazedata$Distance.Spatial2.Block1 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.1,
                                                     watermazedata$Distance.Spatial2.2),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial2.Block2 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.3,
                                                     watermazedata$Distance.Spatial2.4),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial2.Block3 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.5,
                                                     watermazedata$Distance.Spatial2.6),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial2.Block4 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.7,
                                                     watermazedata$Distance.Spatial2.8),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial2.Block5 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.9,
                                                     watermazedata$Distance.Spatial2.10),
                                                   na.rm = TRUE)

watermazedata$Distance.Spatial3.Block1 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.1,
                                                     watermazedata$Distance.Spatial3.2),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial3.Block2 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.3,
                                                     watermazedata$Distance.Spatial3.4),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial3.Block3 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.5,
                                                     watermazedata$Distance.Spatial3.6),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial3.Block4 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.7,
                                                     watermazedata$Distance.Spatial3.8),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial3.Block5 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.9,
                                                     watermazedata$Distance.Spatial3.10),
                                                   na.rm = TRUE)

# Cued, spatial 1, spatial 2 and spatial blocks 1-5 averaged into Overall Averages for both Distance and Duration
watermazedata$Duration.Cued <- rowMeans(cbind (watermazedata$Duration.Cued.1,
                                               watermazedata$Duration.Cued.2,
                                               watermazedata$Duration.Cued.3,
                                               watermazedata$Duration.Cued.4,
                                               watermazedata$Duration.Cued.5),
                                        na.rm = TRUE)
watermazedata$Duration.Spatial1 <- rowMeans(cbind (watermazedata$Duration.Spatial1.1,
                                                   watermazedata$Duration.Spatial1.2,
                                                   watermazedata$Duration.Spatial1.3,
                                                   watermazedata$Duration.Spatial1.4,
                                                   watermazedata$Duration.Spatial1.5),
                                            na.rm = TRUE)
watermazedata$Duration.Spatial2 <- rowMeans(cbind (watermazedata$Duration.Spatial2.1,
                                                   watermazedata$Duration.Spatial2.2,
                                                   watermazedata$Duration.Spatial2.3,
                                                   watermazedata$Duration.Spatial2.4,
                                                   watermazedata$Duration.Spatial2.5),
                                            na.rm = TRUE)
watermazedata$Duration.Spatial3 <- rowMeans(cbind (watermazedata$Duration.Spatial3.1,
                                                   watermazedata$Duration.Spatial3.2,
                                                   watermazedata$Duration.Spatial3.3,
                                                   watermazedata$Duration.Spatial3.4,
                                                   watermazedata$Duration.Spatial3.5),
                                            na.rm = TRUE)
watermazedata$Duration.Spatial <- rowMeans(cbind (watermazedata$Duration.Spatial1,
                                                  watermazedata$Duration.Spatial2,
                                                  watermazedata$Duration.Spatial3),
                                           na.rm = TRUE)


watermazedata$Distance.Cued <- rowMeans(cbind (watermazedata$Distance.Cued.1,
                                               watermazedata$Distance.Cued.2,
                                               watermazedata$Distance.Cued.3,
                                               watermazedata$Distance.Cued.4,
                                               watermazedata$Distance.Cued.5),
                                        na.rm = TRUE)
watermazedata$Distance.Spatial1 <- rowMeans(cbind (watermazedata$Distance.Spatial1.1,
                                                   watermazedata$Distance.Spatial1.2,
                                                   watermazedata$Distance.Spatial1.3,
                                                   watermazedata$Distance.Spatial1.4,
                                                   watermazedata$Distance.Spatial1.5),
                                            na.rm = TRUE)
watermazedata$Distance.Spatial2 <- rowMeans(cbind (watermazedata$Distance.Spatial2.1,
                                                   watermazedata$Distance.Spatial2.2,
                                                   watermazedata$Distance.Spatial2.3,
                                                   watermazedata$Distance.Spatial2.4,
                                                   watermazedata$Distance.Spatial2.5),
                                            na.rm = TRUE)
watermazedata$Distance.Spatial3 <- rowMeans(cbind (watermazedata$Distance.Spatial3.1,
                                                   watermazedata$Distance.Spatial3.2,
                                                   watermazedata$Distance.Spatial3.3,
                                                   watermazedata$Distance.Spatial3.4,
                                                   watermazedata$Distance.Spatial3.5),
                                            na.rm = TRUE)
watermazedata$Distance.Spatial <- rowMeans(cbind (watermazedata$Distance.Spatial1,
                                                  watermazedata$Distance.Spatial2,
                                                  watermazedata$Distance.Spatial3),
                                           na.rm = TRUE)

# Make a Speed variable (Distance/Duration)
watermazedata$Speed <- watermazedata$Distance.Spatial / watermazedata$Duration.Spatial

# Make working memory variables
watermazedata$Working.Duration.Trial1.1 <- watermazedata$Duration.Spatial1.1
watermazedata$Working.Duration.Trial2.1 <- watermazedata$Duration.Spatial1.2
watermazedata$Working.Duration.Diff.1 <- watermazedata$Duration.Spatial1.1 - watermazedata$Duration.Spatial1.2

watermazedata$Working.Duration.Trial1.2 <- watermazedata$Duration.Spatial2.1
watermazedata$Working.Duration.Trial2.2 <- watermazedata$Duration.Spatial2.2
watermazedata$Working.Duration.Diff.2 <- watermazedata$Duration.Spatial2.1 - watermazedata$Duration.Spatial2.2

watermazedata$Working.Duration.Trial1.3 <- watermazedata$Duration.Spatial3.1
watermazedata$Working.Duration.Trial2.3 <- watermazedata$Duration.Spatial3.2
watermazedata$Working.Duration.Diff.3 <- watermazedata$Duration.Spatial3.1 - watermazedata$Duration.Spatial3.2

watermazedata$Working.Duration.Trial1.Ave <- (watermazedata$Duration.Spatial1.1 + watermazedata$Duration.Spatial2.1 + watermazedata$Duration.Spatial3.1) / 3
watermazedata$Working.Duration.Trial2.Ave <- (watermazedata$Duration.Spatial1.2 + watermazedata$Duration.Spatial2.2 + watermazedata$Duration.Spatial3.2) / 3
watermazedata$Working.Duration.Diff.Ave <- (watermazedata$Working.Duration.Diff.1 + watermazedata$Working.Duration.Diff.2 + watermazedata$Working.Duration.Diff.3) / 3


watermazedata$Working.Distance.Trial1.1 <- watermazedata$Distance.Spatial1.1
watermazedata$Working.Distance.Trial2.1 <- watermazedata$Distance.Spatial1.2
watermazedata$Working.Distance.Diff.1 <- watermazedata$Distance.Spatial1.1 - watermazedata$Distance.Spatial1.2

watermazedata$Working.Distance.Trial1.2 <- watermazedata$Distance.Spatial2.1
watermazedata$Working.Distance.Trial2.2 <- watermazedata$Distance.Spatial2.2
watermazedata$Working.Distance.Diff.2 <- watermazedata$Distance.Spatial2.1 - watermazedata$Distance.Spatial2.2

watermazedata$Working.Distance.Trial1.3 <- watermazedata$Distance.Spatial3.1
watermazedata$Working.Distance.Trial2.3 <- watermazedata$Distance.Spatial3.2
watermazedata$Working.Distance.Diff.3 <- watermazedata$Distance.Spatial3.1 - watermazedata$Distance.Spatial3.2

watermazedata$Working.Distance.Trial1.Ave <- (watermazedata$Distance.Spatial1.1 + watermazedata$Distance.Spatial2.1 + watermazedata$Distance.Spatial3.1) / 3
watermazedata$Working.Distance.Trial2.Ave <- (watermazedata$Distance.Spatial1.2 + watermazedata$Distance.Spatial2.2 + watermazedata$Distance.Spatial3.2) / 3
watermazedata$Working.Distance.Diff.Ave <- (watermazedata$Working.Distance.Diff.1 + watermazedata$Working.Distance.Diff.2 + watermazedata$Working.Distance.Diff.3) / 3

Create a “subset” dataframe for each group to ease making histograms/normal curves and QQ plots by group.

Ac_watermazedata<-subset(watermazedata, watermazedata$Treatment=="Ac")
Fx_watermazedata<-subset(watermazedata, watermazedata$Treatment=="Fx")
Sh_watermazedata<-subset(watermazedata, watermazedata$Treatment=="Sh")

How many subjects are missing data from a specific column? (na = ‘missing’). Make a variable that returns 1 (TRUE) if data is missing: watermazedata\(Duration.Spatial1.Missing <- is.na(watermazedata\)Duration.Spatial1)

How many subjects are missing from Duration.Spatial1 data? sum(watermazedata$Duration.Spatial1.Missing)

1 = missing, 0 = there, so mean will tell us proportion of cases missing data in that variable

….or simply calculate this WITHOUT making a new variable:

sum(is.na(watermazedata$Duration.Cued)); mean(is.na(watermazedata$Duration.Cued))
[1] 0
[1] 0
sum(is.na(watermazedata$Duration.Spatial1)); mean(is.na(watermazedata$Duration.Spatial1))
[1] 0
[1] 0
sum(is.na(watermazedata$Duration.Spatial2)); mean(is.na(watermazedata$Duration.Spatial2))
[1] 0
[1] 0
sum(is.na(watermazedata$Duration.Spatial3)); mean(is.na(watermazedata$Duration.Spatial3))
[1] 0
[1] 0
sum(is.na(watermazedata$Duration.Spatial)); mean(is.na(watermazedata$Duration.Spatial))
[1] 0
[1] 0
sum(is.na(watermazedata$Distance.Cued)); mean(is.na(watermazedata$Distance.Cued))
[1] 0
[1] 0
sum(is.na(watermazedata$Distance.Spatial1)); mean(is.na(watermazedata$Distance.Spatial1))
[1] 0
[1] 0
sum(is.na(watermazedata$Distance.Spatial2)); mean(is.na(watermazedata$Distance.Spatial2))
[1] 0
[1] 0
sum(is.na(watermazedata$Distance.Spatial3)); mean(is.na(watermazedata$Distance.Spatial3))
[1] 0
[1] 0
sum(is.na(watermazedata$Distance.Spatial)); mean(is.na(watermazedata$Distance.Spatial))
[1] 0
[1] 0
sum(is.na(watermazedata$Speed)); mean(is.na(watermazedata$Speed))
[1] 0
[1] 0
sum(is.na(watermazedata$Probe.Entries.1)); mean(is.na(watermazedata$Probe.Entries.1))
[1] 0
[1] 0
sum(is.na(watermazedata$Probe.Entries.2)); mean(is.na(watermazedata$Probe.Entries.2))
[1] 0
[1] 0
sum(is.na(watermazedata$Probe.Entries.3)); mean(is.na(watermazedata$Probe.Entries.3))
[1] 0
[1] 0
sum(is.na(watermazedata$Probe.Entries.Ave)); mean(is.na(watermazedata$Probe.Entries.Ave))
[1] 0
[1] 0
sum(is.na(watermazedata$Probe.Percent1)); mean(is.na(watermazedata$Probe.Percent1))
[1] 0
[1] 0
sum(is.na(watermazedata$Probe.Percent2)); mean(is.na(watermazedata$Probe.Percent2))
[1] 0
[1] 0
sum(is.na(watermazedata$Probe.Percent3)); mean(is.na(watermazedata$Probe.Percent3))
[1] 0
[1] 0
sum(is.na(watermazedata$Probe.Percent.Ave)); mean(is.na(watermazedata$Probe.Percent.Ave))
[1] 0
[1] 0
sum(is.na(watermazedata$Probe2.Opposite.Percent)); mean(is.na(watermazedata$Probe2.Opposite.Percent))
[1] 0
[1] 0
sum(is.na(watermazedata$Working.Duration.Trial1.Ave)); mean(is.na(watermazedata$Working.Duration.Trial1.Ave))
[1] 0
[1] 0
sum(is.na(watermazedata$Working.Duration.Trial2.Ave)); mean(is.na(watermazedata$Working.Duration.Trial2.Ave))
[1] 0
[1] 0
sum(is.na(watermazedata$Working.Duration.Diff.Ave)); mean(is.na(watermazedata$Working.Duration.Diff.Ave))
[1] 0
[1] 0
sum(is.na(watermazedata$Working.Distance.Trial1.Ave)); mean(is.na(watermazedata$Working.Distance.Trial1.Ave))
[1] 0
[1] 0
sum(is.na(watermazedata$Working.Distance.Trial2.Ave)); mean(is.na(watermazedata$Working.Distance.Trial2.Ave))
[1] 0
[1] 0
sum(is.na(watermazedata$Working.Distance.Diff.Ave)); mean(is.na(watermazedata$Working.Distance.Diff.Ave))
[1] 0
[1] 0

Now, start checking the various aussumptions (normality, homogeneity of variance, etc.) for all meaningful DVS - e.g., Average Distance and Distance for days 1-3 are probably important, but Blocks 1-5 from each are probably not (?). If the idea is to compare groups, the assumptions need to be tested with each variable broken down by group.

??? is there a “Bonferroni correction” for multiple tests of Normality etc ???

Generate some descriptive stats for the variables of interest.

NOTE: For output reported using “e”: e+02, simply “move” the decimal point 2 places to right. e-02 = move decimal 2 places to left…

Can use describe() (from the psych package) or stat.desc() function (from the pastecs package) to get some basic stats.

# describe()
# Overall DVs (not broken down by group)
# single variables: by(data = dataFrame$Variable, INDICES = dataFrame$grouping DV, FUN = function)
# by(data = watermazedata$Duration.Spatial, INDICES = watermazedata$Treatment, FUN = describe)
# or
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, describe)
# multiple variables at once:
# describe(cbind(watermazedata$Duration.Cued,
#                watermazedata$Duration.Spatial1,
#                watermazedata$Duration.Spatial2,
#                watermazedata$Duration.Spatial3,
#                watermazedata$Duration.Spatial,
#                watermazedata$Distance.Cued,
#                watermazedata$Distance.Spatial1,
#                watermazedata$Distance.Spatial2,
#                watermazedata$Distance.Spatial3,
#                watermazedata$Distance.Spatial,
#                watermazedata$Speed,
#                watermazedata$Probe.Entries.1,
#                watermazedata$Probe.Entries.2,
#                watermazedata$Probe.Entries.3,
#                watermazedata$Probe.Entries.Ave,
#                watermazedata$Probe.Percent1,
#                watermazedata$Probe.Percent2,
#                watermazedata$Probe.Percent3,
#                watermazedata$Probe.Percent.Ave,
#                watermazedata$Probe2.Opposite.Percent,
#                watermazedata$Working.Duration.Trial1.Ave,
#                watermazedata$Working.Duration.Trial2.Ave,
#                watermazedata$Working.Duration.Diff.Ave,
#                watermazedata$Working.Distance.Trial1.Ave,
#                watermazedata$Working.Distance.Trial2.Ave,
#                watermazedata$Working.Distance.Diff.Ave))
# or
# describe(watermazedata[,c("Duration.Spatial1",
#                          "Duration.Spatial2",
#                          "Duration.Spatial3")]); # ETC

# broken down by group
#by(cbind(Duration.Cued=watermazedata$Duration.Cued,
#         Duration.Spatial1=watermazedata$Duration.Spatial1,
#         Duration.Spatial2=watermazedata$Duration.Spatial2,
#         Duration.Spatial3=watermazedata$Duration.Spatial3,
#         Duration.Spatial=watermazedata$Duration.Spatial,
#         Distance.Cued=watermazedata$Distance.Cued,
#         Distance.Spatial1=watermazedata$Distance.Spatial1,
#         Distance.Spatial2=watermazedata$Distance.Spatial2,
#         Distance.Spatial3=watermazedata$Distance.Spatial3,
#         Distance.Spatial=watermazedata$Distance.Spatial,
#         Speed=watermazedata$Speed,
#         Probe.Entries.1=watermazedata$Probe.Entries.1,
#         Probe.Entries.2=watermazedata$Probe.Entries.2,
#         Probe.Entries.3=watermazedata$Probe.Entries.3,
#         Probe.Entries.Ave=watermazedata$Probe.Entries.Ave,
#         Probe.Percent1=watermazedata$Probe.Percent1,
#         Probe.Percent2=watermazedata$Probe.Percent2,
#         Probe.Percent3=watermazedata$Probe.Percent3,
#         Probe.Percent.Ave=watermazedata$Probe.Percent.Ave,
#         Probe2.Opposite.Percent=watermazedata$Probe2.Opposite.Percent,
#         Working.Duration.Trial1.Ave=watermazedata$Working.Duration.Trial1.Ave,
#         Working.Duration.Trial2.Ave=watermazedata$Working.Duration.Trial2.Ave,
#         Working.Duration.Diff.Ave=watermazedata$Working.Duration.Diff.Ave,
#         Working.Distance.Trial1.Ave=watermazedata$Working.Distance.Trial1.Ave,
#         Working.Distance.Trial2.Ave=watermazedata$Working.Distance.Trial2.Ave,
#         Working.Distance.Diff.Ave=watermazedata$Working.Distance.Diff.Ave),
#   watermazedata$Treatment, describe)

# normality of overall variables
# shapiro.test(watermazedata$Duration.Cued)
# shapiro.test(watermazedata$Duration.Spatial1)
# shapiro.test(watermazedata$Duration.Spatial2)
# shapiro.test(watermazedata$Duration.Spatial3)
# shapiro.test(watermazedata$Duration.Spatial)
# shapiro.test(watermazedata$Distance.Cued)
# shapiro.test(watermazedata$Distance.Spatial1)
# shapiro.test(watermazedata$Distance.Spatial2)
# shapiro.test(watermazedata$Distance.Spatial3)
# shapiro.test(watermazedata$Distance.Spatial)
# shapiro.test(watermazedata$Speed)
# shapiro.test(watermazedata$Probe.Entries.1)
# shapiro.test(watermazedata$Probe.Entries.2)
# shapiro.test(watermazedata$Probe.Entries.3)
# shapiro.test(watermazedata$Probe.Entries.Ave)
# shapiro.test(watermazedata$Probe.Percent1)
# shapiro.test(watermazedata$Probe.Percent2)
# shapiro.test(watermazedata$Probe.Percent3)
# shapiro.test(watermazedata$Probe.Percent.Ave)
# shapiro.test(watermazedata$Probe2.Opposite.Percent)
# shapiro.test(watermazedata$Working.Duration.Trial1.Ave)
# shapiro.test(watermazedata$Working.Duration.Trial2.Ave)
# shapiro.test(watermazedata$Working.Duration.Diff.Ave)
# shapiro.test(watermazedata$Working.Distance.Trial1.Ave)
# shapiro.test(watermazedata$Working.Distance.Trial2.Ave)
# shapiro.test(watermazedata$Working.Distance.Diff.Ave)

# normality of variables broken down by group
# by(watermazedata$Duration.Cued, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Cued, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Speed, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe2.Opposite.Percent, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Duration.Trial1.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Duration.Trial2.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Duration.Diff.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Distance.Trial1.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Distance.Trial2.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Distance.Diff.Ave, watermazedata$Treatment, shapiro.test)

Using stat.desc


# stat.desc()
# using basic = FALSE adds Shapiro-Wilks test, negating the need to run that separately as with describe()
# Overall DVs (not broken down by group)
stat.desc(cbind(Duration.Cued=watermazedata$Duration.Cued,
                Duration.Spatial1=watermazedata$Duration.Spatial1,
                Duration.Spatial2=watermazedata$Duration.Spatial2,
                Duration.Spatial3=watermazedata$Duration.Spatial3,
                Duration.Spatial=watermazedata$Duration.Spatial,
                Distance.Cued=watermazedata$Distance.Cued,
                Distance.Spatial1=watermazedata$Distance.Spatial1,
                Distance.Spatial2=watermazedata$Distance.Spatial2,
                Distance.Spatial3=watermazedata$Distance.Spatial3,
                Distance.Spatial=watermazedata$Distance.Spatial,
                Speed=watermazedata$Speed,
                Probe.Entries.1=watermazedata$Probe.Entries.1,
                Probe.Entries.2=watermazedata$Probe.Entries.2,
                Probe.Entries.3=watermazedata$Probe.Entries.3,
                Probe.Entries.Ave=watermazedata$Probe.Entries.Ave,
                Probe.Percent1=watermazedata$Probe.Percent1,
                Probe.Percent2=watermazedata$Probe.Percent2,
                Probe.Percent3=watermazedata$Probe.Percent3,
                Probe.Percent.Ave=watermazedata$Probe.Percent.Ave,
                Probe2.Opposite.Percent=watermazedata$Probe2.Opposite.Percent,
                Working.Duration.Trial1.Ave=watermazedata$Working.Duration.Trial1.Ave,
                Working.Duration.Trial2.Ave=watermazedata$Working.Duration.Trial2.Ave,
                Working.Duration.Diff.Ave=watermazedata$Working.Duration.Diff.Ave,
                Working.Distance.Trial1.Ave=watermazedata$Working.Distance.Trial1.Ave,
                Working.Distance.Trial2.Ave=watermazedata$Working.Distance.Trial2.Ave,
                Working.Distance.Diff.Ave=watermazedata$Working.Distance.Diff.Ave),
          basic = FALSE, norm = TRUE)
# or
# stat.desc(watermazedata[, c("Duration.Spatial1",
#                            "Duration.Spatial2",
#                            "Duration.Spatial3")], basic = FALSE, norm = TRUE); # ETC

# Broken down by group
# single variables: by(data = dataFrame$Variable, INDICES = dataFrame$grouping DV, FUN = function)
# by(data = watermazedata$Duration.Spatial, INDICES = watermazedata$Treatment, FUN = stat.desc)
# or
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, stat.desc)
# or
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, stat.desc, basic = FALSE, norm = TRUE)
# multiple variables at once:
by(cbind(Duration.Cued=watermazedata$Duration.Cued,
         Duration.Spatial1=watermazedata$Duration.Spatial1,
         Duration.Spatial2=watermazedata$Duration.Spatial2,
         Duration.Spatial3=watermazedata$Duration.Spatial3,
         Duration.Spatial=watermazedata$Duration.Spatial,
         Distance.Cued=watermazedata$Distance.Cued,
         Distance.Spatial1=watermazedata$Distance.Spatial1,
         Distance.Spatial2=watermazedata$Distance.Spatial2,
         Distance.Spatial3=watermazedata$Distance.Spatial3,
         Distance.Spatial=watermazedata$Distance.Spatial,
         Speed=watermazedata$Speed,
         Probe.Entries.1=watermazedata$Probe.Entries.1,
         Probe.Entries.2=watermazedata$Probe.Entries.2,
         Probe.Entries.3=watermazedata$Probe.Entries.3,
         Probe.Entries.Ave=watermazedata$Probe.Entries.Ave,
         Probe.Percent1=watermazedata$Probe.Percent1,
         Probe.Percent2=watermazedata$Probe.Percent2,
         Probe.Percent3=watermazedata$Probe.Percent3,
         Probe.Percent.Ave=watermazedata$Probe.Percent.Ave,
         Probe2.Opposite.Percent=watermazedata$Probe2.Opposite.Percent),
   watermazedata$Treatment, stat.desc, basic = FALSE, norm = TRUE)
INDICES: Ac
             Duration.Cued Duration.Spatial1 Duration.Spatial2 Duration.Spatial3
median          22.3900000       14.36000000       15.54000000       17.19000000
mean            23.6470000       14.98000000       18.94100000       21.35800000
SE.mean          1.6968797        1.77887487        2.27763010        2.43156865
CI.mean.0.95     3.5516099        3.72322788        4.76713460        5.08933168
var             57.5880116       63.28791579      103.75197789      118.25052211
std.dev          7.5886765        7.95537025       10.18587148       10.87430559
coef.var         0.3209150        0.53106610        0.53776841        0.50914438
skewness         0.2479948        1.26212085        0.94110222        0.73566280
skew.2SE         0.2421335        1.23229118        0.91885968        0.71827573
kurtosis        -1.1792491        1.97484499        0.31484533       -0.60321900
kurt.2SE        -0.5941498        0.99500081        0.15863086       -0.30392430
normtest.W       0.9608703        0.87806268        0.91348508        0.91465100
normtest.p       0.5613788        0.01633146        0.07429047        0.07820961
             Duration.Spatial Distance.Cued Distance.Spatial1 Distance.Spatial2
median             18.0066667     3.4727000        2.19760000         2.4629000
mean               18.4263333     3.6469100        2.34443000         2.7195600
SE.mean             1.0939434     0.2324343        0.34439751         0.3433361
CI.mean.0.95        2.2896498     0.4864905        0.72083228         0.7186106
var                23.9342432     1.0805138        2.37219293         2.3575929
std.dev             4.8922636     1.0394776        1.54019250         1.5354455
coef.var            0.2655039     0.2850297        0.65695819         0.5645934
skewness            0.2518218     0.5425664        1.34133180         0.8403708
skew.2SE            0.2458701     0.5297431        1.30963002         0.8205091
kurtosis           -0.5840827    -0.8509631        2.25394485         0.3936414
kurt.2SE           -0.2942827    -0.4287470        1.13562176         0.1983313
normtest.W          0.9801489     0.9417958        0.87785717         0.9280982
normtest.p          0.9360099     0.2592150        0.01619371         0.1419282
             Distance.Spatial3 Distance.Spatial         Speed Probe.Entries.1
median              2.34980000        2.6594667  0.1444310179       3.5000000
mean                3.08606000        2.7166833  0.1467410626       3.7000000
SE.mean             0.40930437        0.1810658  0.0023242164       0.4298102
CI.mean.0.95        0.85668390        0.3789751  0.0048646408       0.8996032
var                 3.35060138        0.6556967  0.0001080396       3.6947368
std.dev             1.83046480        0.8097510  0.0103942117       1.9221698
coef.var            0.59313973        0.2980660  0.0708336951       0.5195054
skewness            0.70812629        0.6477020 -0.0297399089       0.2374009
skew.2SE            0.69139004        0.6323939 -0.0290370192       0.2317900
kurtosis           -0.94451685        0.4876389 -0.4892196484      -0.4419827
kurt.2SE           -0.47588294        0.2456907 -0.2464871660      -0.2226874
normtest.W          0.88665782        0.9401439  0.9760920947       0.9704263
normtest.p          0.02335664        0.2412871  0.8743843994       0.7638524
             Probe.Entries.2 Probe.Entries.3 Probe.Entries.Ave Probe.Percent1
median             5.0000000       5.0000000        5.00000000    33.58500000
mean               5.4000000       4.8500000        4.60000000    34.28200000
SE.mean            0.6341177       0.5585460        0.29379549     3.17795114
CI.mean.0.95       1.3272236       1.1690503        0.61492103     6.65152819
var                8.0421053       6.2394737        1.72631579   201.98746947
std.dev            2.8358606       2.4978938        1.31389337    14.21222957
coef.var           0.5251594       0.5150297        0.28562899     0.41456827
skewness           0.7176968       0.3927197       -0.47085901    -0.05372888
skew.2SE           0.7007343       0.3834380       -0.45973047    -0.05245902
kurtosis          -0.3181295      -0.3691493       -0.48631469    -1.14227338
kurt.2SE          -0.1602855      -0.1859912       -0.24502354    -0.57552007
normtest.W         0.9332753       0.9271466        0.90536342     0.97187672
normtest.p         0.1784997       0.1360601        0.05203957     0.79392004
             Probe.Percent2 Probe.Percent3 Probe.Percent.Ave
median          41.25000000    36.50000000        36.7500000
mean            39.20000000    33.88300000        35.7890000
SE.mean          2.56736441     1.85310608         1.2512964
CI.mean.0.95     5.37355546     3.87859561         2.6189934
var            131.82720000    68.68004316        31.3148516
std.dev         11.48160268     8.28734235         5.5959674
coef.var         0.29289803     0.24458703         0.1563600
skewness        -0.94601833    -0.95143892        -0.4572726
skew.2SE        -0.92365960    -0.92895208        -0.4464651
kurtosis         0.29968759    -0.21830216        -0.4986564
kurt.2SE         0.15099382    -0.10998880        -0.2512418
normtest.W       0.91907919     0.88425152         0.9543853
normtest.p       0.09512401     0.02111589         0.4385766
             Probe2.Opposite.Percent
median                    18.0000000
mean                      20.2990000
SE.mean                    1.5917789
CI.mean.0.95               3.3316314
var                       50.6751989
std.dev                    7.1186515
coef.var                   0.3506898
skewness                   0.6760278
skew.2SE                   0.6600502
kurtosis                  -0.5215679
kurt.2SE                  -0.2627854
normtest.W                 0.9295934
normtest.p                 0.1516587
--------------------------------------------------------------- 
INDICES: Fx
             Duration.Cued Duration.Spatial1 Duration.Spatial2 Duration.Spatial3
median         21.73000000        12.4300000      1.755000e+01       21.40000000
mean           23.37222222        12.7988889      2.153222e+01       23.43777778
SE.mean         2.23278843         1.2639398      2.967962e+00        3.15191648
CI.mean.0.95    4.71077180         2.6666798      6.261853e+00        6.64996249
var            89.73619477        28.7557869      1.585584e+02      178.82239477
std.dev         9.47291902         5.3624423      1.259200e+01       13.37244909
coef.var        0.40530673         0.4189772      5.847979e-01        0.57055107
skewness        0.81041711         0.3882022      1.272045e+00        0.50503295
skew.2SE        0.75559436         0.3619413      1.185994e+00        0.47086870
kurtosis        0.10639342        -1.0798882      7.314238e-01       -1.16154284
kurt.2SE        0.05125936        -0.5202800      3.523932e-01       -0.55962051
normtest.W      0.86919556         0.9360768      8.435289e-01        0.90640023
normtest.p      0.01725848         0.2480028      6.706633e-03        0.07435655
             Duration.Spatial Distance.Cued Distance.Spatial1 Distance.Spatial2
median            19.99666667    3.69610000        1.62460000       2.210800000
mean              19.25629630    3.98997778        1.98138889       2.894511111
SE.mean            1.42361882    0.38879126        0.26145987       0.425809855
CI.mean.0.95       3.00357317    0.82027787        0.55163210       0.898380264
var               36.48042992    2.72085565        1.23050271       3.263652580
std.dev            6.03990314    1.64950164        1.10928027       1.806558214
coef.var           0.31365861    0.41341123        0.55984985       0.624132416
skewness          -0.18478275    0.62065506        0.67394281       1.130473609
skew.2SE          -0.17228264    0.57866925        0.62835222       1.053999812
kurtosis          -1.68798788   -0.23498226       -0.86690831       0.205166745
kurt.2SE          -0.81325683   -0.11321226       -0.41766834       0.098847426
normtest.W         0.88880514    0.88847894        0.90114980       0.846707522
normtest.p         0.03681698    0.03634723        0.06017156       0.007517738
             Distance.Spatial3 Distance.Spatial        Speed Probe.Entries.1
median              2.94920000        2.8016333  0.134896814       4.0000000
mean                3.16536667        2.6804222  0.138482061       4.3333333
SE.mean             0.48732298        0.2206222  0.004480923       0.7094138
CI.mean.0.95        1.02816161        0.4654721  0.009453920       1.4967323
var                 4.27470629        0.8761347  0.000361416       9.0588235
std.dev             2.06753628        0.9360207  0.019010944       3.0097880
coef.var            0.65317434        0.3492064  0.137280918       0.6945665
skewness            0.44035925       -0.1121716 -0.109850417       0.5406443
skew.2SE            0.41057002       -0.1045835 -0.102419303       0.5040710
kurtosis           -1.29548429       -1.3864203 -1.287317657      -0.7907476
kurt.2SE           -0.62415226       -0.6679644 -0.620217651      -0.3809748
normtest.W          0.89269316        0.9480545  0.956655741       0.9386126
normtest.p          0.04293112        0.3953187  0.538577561       0.2743302
             Probe.Entries.2 Probe.Entries.3 Probe.Entries.Ave Probe.Percent1
median             4.5000000      4.00000000        4.00000000    35.41500000
mean               4.6111111      3.61111111        4.16666667    39.11166667
SE.mean            0.5724554      0.38039272        0.45911253     3.49474089
CI.mean.0.95       1.2077753      0.80255848        0.96864276     7.37325877
var                5.8986928      2.60457516        3.79411765   219.83785000
std.dev            2.4287225      1.61386962        1.94784949    14.82692989
coef.var           0.5267109      0.44691774        0.46748388     0.37909225
skewness           0.8494462     -0.26808635        0.63646389     1.06294974
skew.2SE           0.7919832     -0.24995096        0.59340865     0.99104377
kurtosis           0.3982610      0.03711183        0.09433583     0.16236708
kurt.2SE           0.1918785      0.01788013        0.04545013     0.07822695
normtest.W         0.9326035      0.94329048        0.91525179     0.88015311
normtest.p         0.2157300      0.32956448        0.10652068     0.02625901
             Probe.Percent2 Probe.Percent3 Probe.Percent.Ave
median          35.50000000     30.5000000      34.775000000
mean            37.07388889     28.4633333      34.882222222
SE.mean          2.92394623      1.9470360       1.891559435
CI.mean.0.95     6.16898729      4.1078868       3.990841562
var            153.89030752     68.2370824      64.403947712
std.dev         12.40525322      8.2605740       8.025207020
coef.var         0.33460890      0.2902181       0.230065819
skewness         0.08594422     -0.9333001      -0.006024109
skew.2SE         0.08013030     -0.8701646      -0.005616592
kurtosis        -0.57837040      0.3594684      -0.002432449
kurt.2SE        -0.27865347      0.1731885      -0.001171931
normtest.W       0.97685565      0.9194261       0.979925163
normtest.p       0.91192507      0.1262816       0.949237025
             Probe2.Opposite.Percent
median                   18.58000000
mean                     20.13000000
SE.mean                   2.23206915
CI.mean.0.95              4.70925426
var                      89.67838824
std.dev                   9.46986738
coef.var                  0.47043554
skewness                  0.70679512
skew.2SE                  0.65898214
kurtosis                  0.05188936
kurt.2SE                  0.02499981
normtest.W                0.94387323
normtest.p                0.33708159
--------------------------------------------------------------- 
INDICES: Sh
             Duration.Cued Duration.Spatial1 Duration.Spatial2 Duration.Spatial3
median         23.06000000       12.08000000        15.5400000        22.4200000
mean           25.09789474       13.82210526        19.0052632        25.8894737
SE.mean         2.63775418        1.50982350         1.8363196         2.4306471
CI.mean.0.95    5.54171590        3.17202147         3.8579644         5.1066001
var           132.19719532       43.31177310        64.0693263       112.2528608
std.dev        11.49770392        6.58116806         8.0043317        10.5949451
coef.var        0.45811428        0.47613355         0.4211640         0.4092376
skewness        0.79198208        0.83572880         0.4171269         0.6594636
skew.2SE        0.75604471        0.79780636         0.3981992         0.6295394
kurtosis       -0.49113414       -0.51161321        -1.1723783         0.9363186
kurt.2SE       -0.24211217       -0.25220764        -0.5779420         0.4615727
normtest.W      0.90845887        0.88784203         0.9197108         0.9276883
normtest.p      0.06936469        0.02947699         0.1117835         0.1569926
             Duration.Spatial Distance.Cued Distance.Spatial1 Distance.Spatial2
median             18.8733333    3.31400000        2.33220000        2.36080000
mean               19.5722807    3.91464211        2.17807368        2.73778947
SE.mean             1.0525935    0.42612757        0.26426884        0.29181399
CI.mean.0.95        2.2114168    0.89526079        0.55520824        0.61307845
var                21.0511075    3.45010934        1.32692242        1.61795274
std.dev             4.5881486    1.85744699        1.15192119        1.27198771
coef.var            0.2344207    0.47448705        0.52887154        0.46460392
skewness            0.5303294    0.89863537        0.76850264        0.61738279
skew.2SE            0.5062650    0.85785845        0.73363069        0.58936812
kurtosis           -0.3497901   -0.42806821       -0.27891088       -0.03764729
kurt.2SE           -0.1724345   -0.21102284       -0.13749343       -0.01855881
normtest.W          0.9634343    0.87435266        0.90134888        0.95201279
normtest.p          0.6416063    0.01714434        0.05146746        0.42730327
             Distance.Spatial3 Distance.Spatial         Speed Probe.Entries.1
median              3.80020000       2.79413333  0.1534098787    5.0000000000
mean                3.98161053       2.96582456  0.1507638122    4.1578947368
SE.mean             0.40007061       0.20119798  0.0037067999    0.5029919715
CI.mean.0.95        0.84051716       0.42270127  0.0077876976    1.0567469189
var                 3.04107338       0.76913191  0.0002610669    4.8070175439
std.dev             1.74386736       0.87700166  0.0161575662    2.1924911730
coef.var            0.43798040       0.29570247  0.1071713824    0.5273080036
skewness            0.27448847       1.30140677 -0.3163322606   -0.8492536425
skew.2SE            0.26203314       1.24235349 -0.3019782125   -0.8107174918
kurtosis            0.04990615       1.89038983  0.4644608256   -0.7281113771
kurt.2SE            0.02460201       0.93189689  0.2289631446   -0.3589337600
normtest.W          0.98061264       0.87112946  0.9445009017    0.7872014119
normtest.p          0.94920809       0.01509693  0.3172627904    0.0007493052
             Probe.Entries.2 Probe.Entries.3 Probe.Entries.Ave Probe.Percent1
median            6.00000000       4.0000000        5.00000000     32.3300000
mean              6.42105263       4.0526316        4.78947368     33.2978947
SE.mean           0.65923839       0.5897451        0.38676154      3.2100484
CI.mean.0.95      1.38500847       1.2390085        0.81255584      6.7440615
var               8.25730994       6.6081871        2.84210526    195.7838064
std.dev           2.87355354       2.5706394        1.68585446     13.9922767
coef.var          0.44752063       0.6343136        0.35199159      0.4202151
skewness          0.38337532       0.2609263        0.51064922     -0.5070319
skew.2SE          0.36597909       0.2490864        0.48747775     -0.4840245
kurtosis         -1.23833007      -1.5641787       -1.00939320     -0.4344221
kurt.2SE         -0.61045395      -0.7710861       -0.49759598     -0.2141551
normtest.W        0.89668584       0.8792907        0.88283992      0.9530587
normtest.p        0.04239384       0.0208689        0.02406722      0.4447327
             Probe.Percent2 Probe.Percent3 Probe.Percent.Ave
median           43.1700000     31.3300000       34.22000000
mean             42.3352632     31.9468421       35.86052632
SE.mean           2.1079617      2.2356719        1.64424594
CI.mean.0.95      4.4286632      4.6969723        3.45443254
var              84.4265485     94.9663450       51.36734971
std.dev           9.1883921      9.7450677        7.16710190
coef.var          0.2170387      0.3050401        0.19986048
skewness         -0.3808935      0.5854291       -0.09526113
skew.2SE         -0.3636099      0.5588644       -0.09093851
kurtosis         -0.9750009     -0.8419064       -1.24496141
kurt.2SE         -0.4806418     -0.4150308       -0.61372297
normtest.W        0.9472765      0.9320952        0.96071802
normtest.p        0.3548309      0.1892693        0.58660667
             Probe2.Opposite.Percent
median                    17.3300000
mean                      17.7100000
SE.mean                    1.2207154
CI.mean.0.95               2.5646280
var                       28.3127778
std.dev                    5.3209753
coef.var                   0.3004503
skewness                   0.4735778
skew.2SE                   0.4520885
kurtosis                  -0.7236955
kurt.2SE                  -0.3567569
normtest.W                 0.9488611
normtest.p                 0.3778937
# or
# by(watermazedata[, c("Duration.Spatial1",
#                     "Duration.Spatial1",
#                     "Duration.Spatial3")],; #ETC
#   watermazedata$Treatment, stat.desc, basic = FALSE, norm = TRUE)

Test for homogeneity of variance among groups using the leveneTest() function from the car package (default uses median)… to use mean instead of median - for example: leveneTest(watermazedata\(Duration.Spatial, watermazedata\)Treatment, center = mean)


leveneTest(watermazedata$Duration.Cued, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.9112 0.4081
      54               
leveneTest(watermazedata$Duration.Spatial1, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.3835 0.6833
      54               
leveneTest(watermazedata$Duration.Spatial2, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2   0.363 0.6973
      54               
leveneTest(watermazedata$Duration.Spatial3, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.9983 0.3752
      54               
leveneTest(watermazedata$Duration.Spatial, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value  Pr(>F)  
group  2  2.4787 0.09335 .
      54                  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
leveneTest(watermazedata$Distance.Cued, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  1.2023 0.3084
      54               
leveneTest(watermazedata$Distance.Spatial1, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.1913 0.8265
      54               
leveneTest(watermazedata$Distance.Spatial2, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.4073 0.6675
      54               
leveneTest(watermazedata$Distance.Spatial3, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.5724 0.5676
      54               
leveneTest(watermazedata$Distance.Spatial, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.9525 0.3921
      54               
leveneTest(watermazedata$Speed, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)  
group  2  3.4883 0.0376 *
      54                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
leveneTest(watermazedata$Probe.Entries.1, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  1.4818 0.2363
      54               
leveneTest(watermazedata$Probe.Entries.2, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.2239 0.8002
      54               
leveneTest(watermazedata$Probe.Entries.3, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2   2.231 0.1172
      54               
leveneTest(watermazedata$Probe.Entries.Ave, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.9072 0.4097
      54               
leveneTest(watermazedata$Probe.Percent1, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.0387  0.962
      54               
leveneTest(watermazedata$Probe.Percent2, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.3962 0.6748
      54               
leveneTest(watermazedata$Probe.Percent3, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.4923 0.6139
      54               
leveneTest(watermazedata$Probe.Percent.Ave, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.9529  0.392
      54               
leveneTest(watermazedata$Probe2.Opposite.Percent, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2   1.456 0.2422
      54               
leveneTest(watermazedata$Working.Duration.Trial1.Ave, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.1796 0.8361
      54               
leveneTest(watermazedata$Working.Duration.Trial2.Ave, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.1042 0.9012
      54               
leveneTest(watermazedata$Working.Duration.Diff.Ave, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2   1.123 0.3328
      54               
leveneTest(watermazedata$Working.Distance.Trial1.Ave, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.3452 0.7096
      54               
leveneTest(watermazedata$Working.Distance.Trial2.Ave, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.0259 0.9745
      54               
leveneTest(watermazedata$Working.Distance.Diff.Ave, watermazedata$Treatment)
watermazedata$Treatment coerced to factor.
Levene's Test for Homogeneity of Variance (center = median)
      Df F value Pr(>F)
group  2  0.8803 0.4205
      54               
# *** IF 1 OF 3 IS SIGNIFICANT [NOT NORMAL], DOES THAT REQUIRE NONPARAMETRIC? ***

Visually check each variable’s data for normality / outliers averaged across all groups. This info is probably not all that interestng until broken down by group. - Histograms w/ overlaid normal curves - Quantile–quantile (QQ) plots - Boxplots - Scatterplots - Violin plots

# Histograms with overlaid normal curve and Quantile–quantile plots
# Scatterplots:
# p <- ggplot(watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
# use geom_crossbar()
# p + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
# Use geom_errorbar()
# p + stat_summary(fun.data=mean_sdl, fun.args = list(mult=1), geom="errorbar", color="red", width=0.2) + stat_summary(fun.y=mean, geom="point", color="red")
# Use geom_pointrange()
# p + stat_summary(fun.data=mean_sdl, fun.args = list(mult=1), geom="pointrange", color="red")

# Duration

hist.Duration.Cued <- ggplot(watermazedata, aes(Duration.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Cued, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Cued, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Cued <- qplot(sample = watermazedata$Duration.Cued)
qqplot.Duration.Cued

boxplot(watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(watermazedata, aes(x=0, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial1 <- ggplot(watermazedata, aes(Duration.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Spatial1, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial1 <- qplot(sample = watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1

boxplot(watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(watermazedata, aes(x=0, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial2 <- ggplot(watermazedata, aes(Duration.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Spatial2, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial2 <- qplot(sample = watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2

boxplot(watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(watermazedata, aes(x=0, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial3 <- ggplot(watermazedata, aes(Duration.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Spatial3, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial3 <- qplot(sample = watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3

boxplot(watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(watermazedata, aes(x=0, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial <- ggplot(watermazedata, aes(Duration.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Spatial, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial <- qplot(sample = watermazedata$Duration.Spatial)
qqplot.Duration.Spatial

boxplot(watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(watermazedata, aes(x=0, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Distance

hist.Distance.Cued <- ggplot(watermazedata, aes(Distance.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Cued, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Cued, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Cued <- qplot(sample = watermazedata$Distance.Cued)
qqplot.Distance.Cued

boxplot(watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(watermazedata, aes(x=0, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial1 <- ggplot(watermazedata, aes(Distance.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Spatial1, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial1 <- qplot(sample = watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1

boxplot(watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(watermazedata, aes(x=0, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial2 <- ggplot(watermazedata, aes(Distance.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Spatial2, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial2 <- qplot(sample = watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2

boxplot(watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(watermazedata, aes(x=0, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial3 <- ggplot(watermazedata, aes(Distance.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Spatial3, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial3 <- qplot(sample = watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3

boxplot(watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(watermazedata, aes(x=0, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial <- ggplot(watermazedata, aes(Distance.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Spatial, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial <- qplot(sample = watermazedata$Distance.Spatial)
qqplot.Distance.Spatial

boxplot(watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(watermazedata, aes(x=0, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Speed

hist.Speed <- ggplot(watermazedata, aes(Speed)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Speed", y = "Number")
hist.Speed +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Speed, na.rm = TRUE),
                  sd = sd(watermazedata$Speed, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Speed <- qplot(sample = watermazedata$Speed)
qqplot.Speed

boxplot(watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")

ggplot(watermazedata, aes(x=0, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Probe stuff

hist.Probe.Entries.1 <- ggplot(watermazedata, aes(Probe.Entries.1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Entries.1, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Entries.1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.1 <- qplot(sample = watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1

boxplot(watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(watermazedata, aes(x=0, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Probe.Entries.2 <- ggplot(watermazedata, aes(Probe.Entries.2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Entries.2, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Entries.2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.2 <- qplot(sample = watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2

boxplot(watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(watermazedata, aes(x=0, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Probe.Entries.3 <- ggplot(watermazedata, aes(Probe.Entries.3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Entries.3, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Entries.3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.3 <- qplot(sample = watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3

boxplot(watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(watermazedata, aes(x=0, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Probe.Entries.Ave <- ggplot(watermazedata, aes(Probe.Entries.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Entries.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.Ave <- qplot(sample = watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave

boxplot(watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(watermazedata, aes(x=0, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Probe.Percent1 <- ggplot(watermazedata, aes(Probe.Percent1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Percent1, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Percent1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent1 <- qplot(sample = watermazedata$Probe.Percent1)
qqplot.Probe.Percent1

boxplot(watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(watermazedata, aes(x=0, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Probe.Percent2 <- ggplot(watermazedata, aes(Probe.Percent2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Percent2, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Percent2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent2 <- qplot(sample = watermazedata$Probe.Percent2)
qqplot.Probe.Percent2

boxplot(watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(watermazedata, aes(x=0, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Probe.Percent3 <- ggplot(watermazedata, aes(Probe.Percent3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Percent3, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Percent3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent3 <- qplot(sample = watermazedata$Probe.Percent3)
qqplot.Probe.Percent3

boxplot(watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(watermazedata, aes(x=0, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Probe.Percent.Ave <- ggplot(watermazedata, aes(Probe.Percent.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Percent.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent.Ave <- qplot(sample = watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave

boxplot(watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(watermazedata, aes(x=0, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe2.Opposite.Percent <- ggplot(watermazedata, aes(Probe2.Opposite.Percent)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
                  sd = sd(watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe2.Opposite.Percent <- qplot(sample = watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent

boxplot(watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(watermazedata, aes(x=0, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



# Working memory stuff

hist.Working.Duration.Trial1.Ave <- ggplot(watermazedata, aes(Working.Duration.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Duration.Trial1.Ave <- qplot(sample = watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave

boxplot(watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Working.Duration.Trial2.Ave <- ggplot(watermazedata, aes(Working.Duration.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Duration.Trial2.Ave <- qplot(sample = watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave

boxplot(watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Duration.Diff.Ave <- ggplot(watermazedata, aes(Working.Duration.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Duration.Diff.Ave <- qplot(sample = watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave

boxplot(watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(watermazedata, aes(x=0, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Working.Distance.Trial1.Ave <- ggplot(watermazedata, aes(Working.Distance.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Distance.Trial1.Ave <- qplot(sample = watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave

boxplot(watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Working.Distance.Trial2.Ave <- ggplot(watermazedata, aes(Working.Distance.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Distance.Trial2.Ave <- qplot(sample = watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave

boxplot(watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Working.Distance.Diff.Ave <- ggplot(watermazedata, aes(Working.Distance.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Distance.Diff.Ave <- qplot(sample = watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave

boxplot(watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(watermazedata, aes(x=0, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=0, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=0, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

Now, visually check each variable data for normality / outliers broken down by group - histograms and QQ plots and box plots for each Treatment group.

# Broken down by group (use the "subset" dataframes that were derived earlier)

# Ac
# Duration

hist.Duration.Cued <- ggplot(Ac_watermazedata, aes(Duration.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Cued, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Cued, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Cued <- qplot(sample = Ac_watermazedata$Duration.Cued)
qqplot.Duration.Cued

boxplot(Ac_watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Duration.Spatial1 <- ggplot(Ac_watermazedata, aes(Duration.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Spatial1, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial1 <- qplot(sample = Ac_watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1

boxplot(Ac_watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Duration.Spatial2 <- ggplot(Ac_watermazedata, aes(Duration.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Spatial2, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial2 <- qplot(sample = Ac_watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2

boxplot(Ac_watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial3 <- ggplot(Ac_watermazedata, aes(Duration.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Spatial3, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial3 <- qplot(sample = Ac_watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3

boxplot(Ac_watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial <- ggplot(Ac_watermazedata, aes(Duration.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Spatial, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial <- qplot(sample = Ac_watermazedata$Duration.Spatial)
qqplot.Duration.Spatial

boxplot(Ac_watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Distance

hist.Distance.Cued <- ggplot(Ac_watermazedata, aes(Distance.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Cued, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Cued, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Cued <- qplot(sample = Ac_watermazedata$Distance.Cued)
qqplot.Distance.Cued

boxplot(Ac_watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial1 <- ggplot(Ac_watermazedata, aes(Distance.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Spatial1, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial1 <- qplot(sample = Ac_watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1

boxplot(Ac_watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial2 <- ggplot(Ac_watermazedata, aes(Distance.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Spatial2, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial2 <- qplot(sample = Ac_watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2

boxplot(Ac_watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial3 <- ggplot(Ac_watermazedata, aes(Distance.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Spatial3, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial3 <- qplot(sample = Ac_watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3

boxplot(Ac_watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial <- ggplot(Ac_watermazedata, aes(Distance.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Spatial, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial <- qplot(sample = Ac_watermazedata$Distance.Spatial)
qqplot.Distance.Spatial

boxplot(Ac_watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Speed

hist.Speed <- ggplot(Ac_watermazedata, aes(Speed)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Speed", y = "Number")
hist.Speed +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Speed, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Speed, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Speed <- qplot(sample = Ac_watermazedata$Speed)
qqplot.Speed

boxplot(Ac_watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



# Probe stuff

hist.Probe.Entries.1 <- ggplot(Ac_watermazedata, aes(Probe.Entries.1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Entries.1, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Entries.1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.1 <- qplot(sample = Ac_watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1

boxplot(Ac_watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.2 <- ggplot(Ac_watermazedata, aes(Probe.Entries.2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Entries.2, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Entries.2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.2 <- qplot(sample = Ac_watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2

boxplot(Ac_watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.3 <- ggplot(Ac_watermazedata, aes(Probe.Entries.3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Entries.3, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Entries.3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.3 <- qplot(sample = Ac_watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3

boxplot(Ac_watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.Ave <- ggplot(Ac_watermazedata, aes(Probe.Entries.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Entries.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.Ave <- qplot(sample = Ac_watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave

boxplot(Ac_watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent1 <- ggplot(Ac_watermazedata, aes(Probe.Percent1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Percent1, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Percent1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent1 <- qplot(sample = Ac_watermazedata$Probe.Percent1)
qqplot.Probe.Percent1

boxplot(Ac_watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent2 <- ggplot(Ac_watermazedata, aes(Probe.Percent2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Percent2, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Percent2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent2 <- qplot(sample = Ac_watermazedata$Probe.Percent2)
qqplot.Probe.Percent2

boxplot(Ac_watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent3 <- ggplot(Ac_watermazedata, aes(Probe.Percent3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Percent3, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Percent3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent3 <- qplot(sample = Ac_watermazedata$Probe.Percent3)
qqplot.Probe.Percent3

boxplot(Ac_watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent.Ave <- ggplot(Ac_watermazedata, aes(Probe.Percent.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Percent.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent.Ave <- qplot(sample = Ac_watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave

boxplot(Ac_watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe2.Opposite.Percent <- ggplot(Ac_watermazedata, aes(Probe2.Opposite.Percent)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe2.Opposite.Percent <- qplot(sample = Ac_watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent

boxplot(Ac_watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Working memory stuff

hist.Working.Duration.Trial1.Ave <- ggplot(Ac_watermazedata, aes(Working.Duration.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Duration.Trial1.Ave <- qplot(sample = Ac_watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave

boxplot(Ac_watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Duration.Trial2.Ave <- ggplot(Ac_watermazedata, aes(Working.Duration.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Duration.Trial2.Ave <- qplot(sample = Ac_watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave

boxplot(Ac_watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Duration.Diff.Ave <- ggplot(Ac_watermazedata, aes(Working.Duration.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Duration.Diff.Ave <- qplot(sample = Ac_watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave

boxplot(Ac_watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Trial1.Ave <- ggplot(Ac_watermazedata, aes(Working.Distance.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Distance.Trial1.Ave <- qplot(sample = Ac_watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave

boxplot(Ac_watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Trial2.Ave <- ggplot(Ac_watermazedata, aes(Working.Distance.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Distance.Trial2.Ave <- qplot(sample = Ac_watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave

boxplot(Ac_watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Diff.Ave <- ggplot(Ac_watermazedata, aes(Working.Distance.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Distance.Diff.Ave <- qplot(sample = Ac_watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave

boxplot(Ac_watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Fx
# Duration

hist.Duration.Cued <- ggplot(Fx_watermazedata, aes(Duration.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Cued, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Cued, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Cued <- qplot(sample = Fx_watermazedata$Duration.Cued)
qqplot.Duration.Cued

boxplot(Fx_watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Duration.Spatial1 <- ggplot(Fx_watermazedata, aes(Duration.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Spatial1, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial1 <- qplot(sample = Fx_watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1

boxplot(Fx_watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Duration.Spatial2 <- ggplot(Fx_watermazedata, aes(Duration.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Spatial2, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial2 <- qplot(sample = Fx_watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2

boxplot(Fx_watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial3 <- ggplot(Fx_watermazedata, aes(Duration.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Spatial3, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial3 <- qplot(sample = Fx_watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3

boxplot(Fx_watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial <- ggplot(Fx_watermazedata, aes(Duration.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Spatial, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial <- qplot(sample = Fx_watermazedata$Duration.Spatial)
qqplot.Duration.Spatial

boxplot(Fx_watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Distance

hist.Distance.Cued <- ggplot(Fx_watermazedata, aes(Distance.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Cued, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Cued, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Cued <- qplot(sample = Fx_watermazedata$Distance.Cued)
qqplot.Distance.Cued

boxplot(Fx_watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial1 <- ggplot(Fx_watermazedata, aes(Distance.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Spatial1, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial1 <- qplot(sample = Fx_watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1

boxplot(Fx_watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial2 <- ggplot(Fx_watermazedata, aes(Distance.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Spatial2, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial2 <- qplot(sample = Fx_watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2

boxplot(Fx_watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial3 <- ggplot(Fx_watermazedata, aes(Distance.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Spatial3, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial3 <- qplot(sample = Fx_watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3

boxplot(Fx_watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial <- ggplot(Fx_watermazedata, aes(Distance.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Spatial, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial <- qplot(sample = Fx_watermazedata$Distance.Spatial)
qqplot.Distance.Spatial

boxplot(Fx_watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Speed

hist.Speed <- ggplot(Fx_watermazedata, aes(Speed)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Speed", y = "Number")
hist.Speed +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Speed, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Speed, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Speed <- qplot(sample = Fx_watermazedata$Speed)
qqplot.Speed

boxplot(Fx_watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



# Probe stuff

hist.Probe.Entries.1 <- ggplot(Fx_watermazedata, aes(Probe.Entries.1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Entries.1, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Entries.1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.1 <- qplot(sample = Fx_watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1

boxplot(Fx_watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.2 <- ggplot(Fx_watermazedata, aes(Probe.Entries.2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Entries.2, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Entries.2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.2 <- qplot(sample = Fx_watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2

boxplot(Fx_watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.3 <- ggplot(Fx_watermazedata, aes(Probe.Entries.3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Entries.3, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Entries.3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.3 <- qplot(sample = Fx_watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3

boxplot(Fx_watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.Ave <- ggplot(Fx_watermazedata, aes(Probe.Entries.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Entries.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.Ave <- qplot(sample = Fx_watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave

boxplot(Fx_watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent1 <- ggplot(Fx_watermazedata, aes(Probe.Percent1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Percent1, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Percent1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent1 <- qplot(sample = Fx_watermazedata$Probe.Percent1)
qqplot.Probe.Percent1

boxplot(Fx_watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent2 <- ggplot(Fx_watermazedata, aes(Probe.Percent2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Percent2, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Percent2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent2 <- qplot(sample = Fx_watermazedata$Probe.Percent2)
qqplot.Probe.Percent2

boxplot(Fx_watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent3 <- ggplot(Fx_watermazedata, aes(Probe.Percent3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Percent3, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Percent3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent3 <- qplot(sample = Fx_watermazedata$Probe.Percent3)
qqplot.Probe.Percent3

boxplot(Fx_watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent.Ave <- ggplot(Fx_watermazedata, aes(Probe.Percent.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Percent.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent.Ave <- qplot(sample = Fx_watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave

boxplot(Fx_watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe2.Opposite.Percent <- ggplot(Fx_watermazedata, aes(Probe2.Opposite.Percent)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe2.Opposite.Percent <- qplot(sample = Fx_watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent

boxplot(Fx_watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Working memory stuff

hist.Working.Duration.Trial1.Ave <- ggplot(Fx_watermazedata, aes(Working.Duration.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Duration.Trial1.Ave <- qplot(sample = Fx_watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave

boxplot(Fx_watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Duration.Trial2.Ave <- ggplot(Fx_watermazedata, aes(Working.Duration.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Duration.Trial2.Ave <- qplot(sample = Fx_watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave

boxplot(Fx_watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Duration.Diff.Ave <- ggplot(Fx_watermazedata, aes(Working.Duration.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Duration.Diff.Ave <- qplot(sample = Fx_watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave

boxplot(Fx_watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Trial1.Ave <- ggplot(Fx_watermazedata, aes(Working.Distance.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Distance.Trial1.Ave <- qplot(sample = Fx_watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave

boxplot(Fx_watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Trial2.Ave <- ggplot(Fx_watermazedata, aes(Working.Distance.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Distance.Trial2.Ave <- qplot(sample = Fx_watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave

boxplot(Fx_watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Diff.Ave <- ggplot(Fx_watermazedata, aes(Working.Distance.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Distance.Diff.Ave <- qplot(sample = Fx_watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave

boxplot(Fx_watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Sh
# Duration

hist.Duration.Cued <- ggplot(Sh_watermazedata, aes(Duration.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Cued, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Cued, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Cued <- qplot(sample = Sh_watermazedata$Duration.Cued)
qqplot.Duration.Cued

boxplot(Sh_watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Duration.Spatial1 <- ggplot(Sh_watermazedata, aes(Duration.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Spatial1, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial1 <- qplot(sample = Sh_watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1

boxplot(Sh_watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



hist.Duration.Spatial2 <- ggplot(Sh_watermazedata, aes(Duration.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Spatial2, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial2 <- qplot(sample = Sh_watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2

boxplot(Sh_watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial3 <- ggplot(Sh_watermazedata, aes(Duration.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Spatial3, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial3 <- qplot(sample = Sh_watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3

boxplot(Sh_watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial <- ggplot(Sh_watermazedata, aes(Duration.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Spatial, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Duration.Spatial <- qplot(sample = Sh_watermazedata$Duration.Spatial)
qqplot.Duration.Spatial

boxplot(Sh_watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Distance

hist.Distance.Cued <- ggplot(Sh_watermazedata, aes(Distance.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Cued, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Cued, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Cued <- qplot(sample = Sh_watermazedata$Distance.Cued)
qqplot.Distance.Cued

boxplot(Sh_watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial1 <- ggplot(Sh_watermazedata, aes(Distance.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Spatial1, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial1 <- qplot(sample = Sh_watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1

boxplot(Sh_watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial2 <- ggplot(Sh_watermazedata, aes(Distance.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Spatial2, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial2 <- qplot(sample = Sh_watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2

boxplot(Sh_watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial3 <- ggplot(Sh_watermazedata, aes(Distance.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Spatial3, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial3 <- qplot(sample = Sh_watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3

boxplot(Sh_watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Distance.Spatial <- ggplot(Sh_watermazedata, aes(Distance.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Spatial, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Distance.Spatial <- qplot(sample = Sh_watermazedata$Distance.Spatial)
qqplot.Distance.Spatial

boxplot(Sh_watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Speed

hist.Speed <- ggplot(Sh_watermazedata, aes(Speed)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Speed", y = "Number")
hist.Speed +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Speed, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Speed, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Speed <- qplot(sample = Sh_watermazedata$Speed)
qqplot.Speed

boxplot(Sh_watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")



# Probe stuff

hist.Probe.Entries.1 <- ggplot(Sh_watermazedata, aes(Probe.Entries.1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Entries.1, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Entries.1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.1 <- qplot(sample = Sh_watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1

boxplot(Sh_watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.2 <- ggplot(Sh_watermazedata, aes(Probe.Entries.2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Entries.2, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Entries.2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.2 <- qplot(sample = Sh_watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2

boxplot(Sh_watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.3 <- ggplot(Sh_watermazedata, aes(Probe.Entries.3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Entries.3, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Entries.3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.3 <- qplot(sample = Sh_watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3

boxplot(Sh_watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.Ave <- ggplot(Sh_watermazedata, aes(Probe.Entries.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Entries.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Entries.Ave <- qplot(sample = Sh_watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave

boxplot(Sh_watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent1 <- ggplot(Sh_watermazedata, aes(Probe.Percent1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Percent1, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Percent1, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent1 <- qplot(sample = Sh_watermazedata$Probe.Percent1)
qqplot.Probe.Percent1

boxplot(Sh_watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent2 <- ggplot(Sh_watermazedata, aes(Probe.Percent2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Percent2, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Percent2, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent2 <- qplot(sample = Sh_watermazedata$Probe.Percent2)
qqplot.Probe.Percent2

boxplot(Sh_watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent3 <- ggplot(Sh_watermazedata, aes(Probe.Percent3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Percent3, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Percent3, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent3 <- qplot(sample = Sh_watermazedata$Probe.Percent3)
qqplot.Probe.Percent3

boxplot(Sh_watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent.Ave <- ggplot(Sh_watermazedata, aes(Probe.Percent.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Percent.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe.Percent.Ave <- qplot(sample = Sh_watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave

boxplot(Sh_watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe2.Opposite.Percent <- ggplot(Sh_watermazedata, aes(Probe2.Opposite.Percent)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Probe2.Opposite.Percent <- qplot(sample = Sh_watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent

boxplot(Sh_watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Working memory stuff

hist.Working.Duration.Trial1.Ave <- ggplot(Sh_watermazedata, aes(Working.Duration.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Duration.Trial1.Ave <- qplot(sample = Sh_watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave

boxplot(Sh_watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Duration.Trial2.Ave <- ggplot(Sh_watermazedata, aes(Working.Duration.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Duration.Trial2.Ave <- qplot(sample = Sh_watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave

boxplot(Sh_watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Duration.Diff.Ave <- ggplot(Sh_watermazedata, aes(Working.Duration.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Duration.Diff.Ave <- qplot(sample = Sh_watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave

boxplot(Sh_watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Trial1.Ave <- ggplot(Sh_watermazedata, aes(Working.Distance.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Distance.Trial1.Ave <- qplot(sample = Sh_watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave

boxplot(Sh_watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Trial2.Ave <- ggplot(Sh_watermazedata, aes(Working.Distance.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Distance.Trial2.Ave <- qplot(sample = Sh_watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave

boxplot(Sh_watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Diff.Ave <- ggplot(Sh_watermazedata, aes(Working.Distance.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)

qqplot.Working.Distance.Diff.Ave <- qplot(sample = Sh_watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave

boxplot(Sh_watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

Now visually compare groups against each other

# Broken down by group all on 1 graph

# Duration

ggplot(watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Distance

ggplot(watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Speed

ggplot(watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Probe stuff

ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Working memory stuff

ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")

scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")

ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

Meeting assumptions of normality / homogeneity of variance can be tough w/ large data sets because small variations can be “significant” (you can also test homogeneity of variance w/ “variance ratio” or Hartley’s Fmax). Either way, if data are not normally distributed and of equal variances, parametric tests are not valid. To correct “problems” with the data:

Outliers - remove the case / subject (especially if it was somehow “different”) - “bring the case it into the fold” using the mean + 2 or 3 SDs - Change the score to be the mean + 2 or 3 SDs - “bring the case into the fold” using the next highest score plus one method - Change the score to be one unit above the next highest score in the data set

For non-normally-distributed data: - Can also use “trimmed means” (removing a specific % of cases have been removed from each end) - Can also use “M-estimator” which empirically derives the proper % to trim - Can also use bootstrapping to estimate “true” mean / variance - Transform the data: log, square root, or reciprocal transformations can correct for positive skew and/or unequal variance. If data are negatively skewed, you need derive a reciprocal score (reverse the scores by subtracting each score from the highest score obtained) – Make new transformed DVs using newVariable <- function(oldVariable) — Square root: watermazedata\(Duration.Spatial.Sqrt <- sqrt(watermazedata\)Duration.Spatial) — Absolute value: watermazedata\(Duration.Spatial.Abs <- abs(watermazedata\)Duration.Spatial.Diff) — Log (natural): watermazedata\(Duration.Spatial.Log <- log(watermazedata\)Duration.Spatial +1) +1 needed to avoid trying to calculate log of 0 — Log (base 10): watermazedata\(Duration.Spatial.Log10 <- log10(watermazedata\)Duration.Spatial) +1 needed for base 10??? — Reciprocal: watermazedata\(Duration.Spatial.Reciprocal <- 1/(watermazedata\)Duration.Spatial +1) +1 needed to avoid trying to divide by zero

---
title: "R Notebook - EDA workflow template using Nelson data"
author: "Rich Hartman, PhD"
date: "December 18th, 2019"
output:
  html_notebook: default
  word_document: default
---

install required packages (only need to do this once?)

install.packages("ggplot2"); # for graphics functions
install.packages("car"); # for the leveneTest() function
install.packages("pastecs"); # for the stat.desc() function
install.packages("psych"); # for the describe() function
install.packages("hrbrthemes")
install.packages("viridis")

"call" the required packages (need to do this every session?)

```{r}
library(car); library(ggplot2); library(pastecs); library(psych); library(hrbrthemes); library(viridis)
```

Use Excel to generate a .csv file with "tidy" data (each row = 1 case / subject, 1st row is column names). Import .CSV file into R "dataframe" called "watermazedata". Then show the "watermazedata" dataframe (header + 1st 8 data rows) to check it out

```{r}
watermazedata <- read.csv(file="./data_clean/water maze all.csv", header=TRUE, sep=",")
watermazedata
```

Derive new variables. Mostly use the rowMeans() function, but these may be useful as well...

Make some new DVs (~"columns") assigned 1 (TRUE) or 0 (FALSE) based on Boolean calculations:
- Less than? watermazedata$Duration.Spatial2LessThanSpatial1 <- watermazedata$Duration.Spatial2 < watermazedata$Duration.Spatial2
- Less than or equal to? watermazedata$Duration.Spatial1LessThanOrEqualTo60 <- watermazedata$Duration.Spatial2 <= 60
- Equal to? watermazedata$Sh <- watermazedata$Treatment == "Sh"
- Not equal to? watermazedata$NotSh <- watermazedata$Treatment != "Sh"

Use "ifelse" to maybe replace scores (e.g., replace any duration greater than 60 with NA "missing data", else keep the same)
- watermazedata$Duration.Spatial.Clean <- ifelse(watermazedata$Duration.Spatial > 60, NA, watermazedata$Duration.Spatial)

```{r}
# Trials 1-10 for cued, spatial 1, spatial 2 and spatial trials averaged into 5 blocks (2 trials each) each for both Distance and Duration
watermazedata$Duration.Cued.Block1 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.1,
                                                 watermazedata$Duration.Cued.2),
                                               na.rm = TRUE)
watermazedata$Duration.Cued.Block2 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.3,
                                                 watermazedata$Duration.Cued.4),
                                               na.rm = TRUE)
watermazedata$Duration.Cued.Block3 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.5,
                                                 watermazedata$Duration.Cued.6),
                                               na.rm = TRUE)
watermazedata$Duration.Cued.Block4 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.7,
                                                 watermazedata$Duration.Cued.8),
                                               na.rm = TRUE)
watermazedata$Duration.Cued.Block5 <- rowMeans(cbind
                                               (watermazedata$Duration.Cued.9,
                                                 watermazedata$Duration.Cued.10),
                                               na.rm = TRUE)

watermazedata$Duration.Spatial1.Block1 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.1,
                                                     watermazedata$Duration.Spatial1.2),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial1.Block2 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.3,
                                                     watermazedata$Duration.Spatial1.4),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial1.Block3 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.5,
                                                     watermazedata$Duration.Spatial1.6),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial1.Block4 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.7,
                                                     watermazedata$Duration.Spatial1.8),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial1.Block5 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial1.9,
                                                     watermazedata$Duration.Spatial1.10),
                                                   na.rm = TRUE)

watermazedata$Duration.Spatial2.Block1 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.1,
                                                     watermazedata$Duration.Spatial2.2),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial2.Block2 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.3,
                                                     watermazedata$Duration.Spatial2.4),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial2.Block3 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.5,
                                                     watermazedata$Duration.Spatial2.6),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial2.Block4 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.7,
                                                     watermazedata$Duration.Spatial2.8),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial2.Block5 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial2.9,
                                                     watermazedata$Duration.Spatial2.10),
                                                   na.rm = TRUE)

watermazedata$Duration.Spatial3.Block1 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.1,
                                                     watermazedata$Duration.Spatial3.2),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial3.Block2 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.3,
                                                     watermazedata$Duration.Spatial3.4),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial3.Block3 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.5,
                                                     watermazedata$Duration.Spatial3.6),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial3.Block4 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.7,
                                                     watermazedata$Duration.Spatial3.8),
                                                   na.rm = TRUE)
watermazedata$Duration.Spatial3.Block5 <- rowMeans(cbind
                                                   (watermazedata$Duration.Spatial3.9,
                                                     watermazedata$Duration.Spatial3.10),
                                                   na.rm = TRUE)

watermazedata$Distance.Cued.Block1 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.1,
                                                 watermazedata$Distance.Cued.2),
                                               na.rm = TRUE)
watermazedata$Distance.Cued.Block2 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.3,
                                                 watermazedata$Distance.Cued.4),
                                               na.rm = TRUE)
watermazedata$Distance.Cued.Block3 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.5,
                                                 watermazedata$Distance.Cued.6),
                                               na.rm = TRUE)
watermazedata$Distance.Cued.Block4 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.7,
                                                 watermazedata$Distance.Cued.8),
                                               na.rm = TRUE)
watermazedata$Distance.Cued.Block5 <- rowMeans(cbind
                                               (watermazedata$Distance.Cued.9,
                                                 watermazedata$Distance.Cued.10),
                                               na.rm = TRUE)

watermazedata$Distance.Spatial1.Block1 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.1,
                                                     watermazedata$Distance.Spatial1.2),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial1.Block2 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.3,
                                                     watermazedata$Distance.Spatial1.4),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial1.Block3 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.5,
                                                     watermazedata$Distance.Spatial1.6),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial1.Block4 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.7,
                                                     watermazedata$Distance.Spatial1.8),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial1.Block5 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial1.9,
                                                     watermazedata$Distance.Spatial1.10),
                                                   na.rm = TRUE)

watermazedata$Distance.Spatial2.Block1 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.1,
                                                     watermazedata$Distance.Spatial2.2),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial2.Block2 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.3,
                                                     watermazedata$Distance.Spatial2.4),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial2.Block3 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.5,
                                                     watermazedata$Distance.Spatial2.6),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial2.Block4 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.7,
                                                     watermazedata$Distance.Spatial2.8),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial2.Block5 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial2.9,
                                                     watermazedata$Distance.Spatial2.10),
                                                   na.rm = TRUE)

watermazedata$Distance.Spatial3.Block1 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.1,
                                                     watermazedata$Distance.Spatial3.2),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial3.Block2 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.3,
                                                     watermazedata$Distance.Spatial3.4),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial3.Block3 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.5,
                                                     watermazedata$Distance.Spatial3.6),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial3.Block4 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.7,
                                                     watermazedata$Distance.Spatial3.8),
                                                   na.rm = TRUE)
watermazedata$Distance.Spatial3.Block5 <- rowMeans(cbind
                                                   (watermazedata$Distance.Spatial3.9,
                                                     watermazedata$Distance.Spatial3.10),
                                                   na.rm = TRUE)

# Cued, spatial 1, spatial 2 and spatial blocks 1-5 averaged into Overall Averages for both Distance and Duration
watermazedata$Duration.Cued <- rowMeans(cbind (watermazedata$Duration.Cued.1,
                                               watermazedata$Duration.Cued.2,
                                               watermazedata$Duration.Cued.3,
                                               watermazedata$Duration.Cued.4,
                                               watermazedata$Duration.Cued.5),
                                        na.rm = TRUE)
watermazedata$Duration.Spatial1 <- rowMeans(cbind (watermazedata$Duration.Spatial1.1,
                                                   watermazedata$Duration.Spatial1.2,
                                                   watermazedata$Duration.Spatial1.3,
                                                   watermazedata$Duration.Spatial1.4,
                                                   watermazedata$Duration.Spatial1.5),
                                            na.rm = TRUE)
watermazedata$Duration.Spatial2 <- rowMeans(cbind (watermazedata$Duration.Spatial2.1,
                                                   watermazedata$Duration.Spatial2.2,
                                                   watermazedata$Duration.Spatial2.3,
                                                   watermazedata$Duration.Spatial2.4,
                                                   watermazedata$Duration.Spatial2.5),
                                            na.rm = TRUE)
watermazedata$Duration.Spatial3 <- rowMeans(cbind (watermazedata$Duration.Spatial3.1,
                                                   watermazedata$Duration.Spatial3.2,
                                                   watermazedata$Duration.Spatial3.3,
                                                   watermazedata$Duration.Spatial3.4,
                                                   watermazedata$Duration.Spatial3.5),
                                            na.rm = TRUE)
watermazedata$Duration.Spatial <- rowMeans(cbind (watermazedata$Duration.Spatial1,
                                                  watermazedata$Duration.Spatial2,
                                                  watermazedata$Duration.Spatial3),
                                           na.rm = TRUE)


watermazedata$Distance.Cued <- rowMeans(cbind (watermazedata$Distance.Cued.1,
                                               watermazedata$Distance.Cued.2,
                                               watermazedata$Distance.Cued.3,
                                               watermazedata$Distance.Cued.4,
                                               watermazedata$Distance.Cued.5),
                                        na.rm = TRUE)
watermazedata$Distance.Spatial1 <- rowMeans(cbind (watermazedata$Distance.Spatial1.1,
                                                   watermazedata$Distance.Spatial1.2,
                                                   watermazedata$Distance.Spatial1.3,
                                                   watermazedata$Distance.Spatial1.4,
                                                   watermazedata$Distance.Spatial1.5),
                                            na.rm = TRUE)
watermazedata$Distance.Spatial2 <- rowMeans(cbind (watermazedata$Distance.Spatial2.1,
                                                   watermazedata$Distance.Spatial2.2,
                                                   watermazedata$Distance.Spatial2.3,
                                                   watermazedata$Distance.Spatial2.4,
                                                   watermazedata$Distance.Spatial2.5),
                                            na.rm = TRUE)
watermazedata$Distance.Spatial3 <- rowMeans(cbind (watermazedata$Distance.Spatial3.1,
                                                   watermazedata$Distance.Spatial3.2,
                                                   watermazedata$Distance.Spatial3.3,
                                                   watermazedata$Distance.Spatial3.4,
                                                   watermazedata$Distance.Spatial3.5),
                                            na.rm = TRUE)
watermazedata$Distance.Spatial <- rowMeans(cbind (watermazedata$Distance.Spatial1,
                                                  watermazedata$Distance.Spatial2,
                                                  watermazedata$Distance.Spatial3),
                                           na.rm = TRUE)

# Make a Speed variable (Distance/Duration)
watermazedata$Speed <- watermazedata$Distance.Spatial / watermazedata$Duration.Spatial

# Make working memory variables
watermazedata$Working.Duration.Trial1.1 <- watermazedata$Duration.Spatial1.1
watermazedata$Working.Duration.Trial2.1 <- watermazedata$Duration.Spatial1.2
watermazedata$Working.Duration.Diff.1 <- watermazedata$Duration.Spatial1.1 - watermazedata$Duration.Spatial1.2

watermazedata$Working.Duration.Trial1.2 <- watermazedata$Duration.Spatial2.1
watermazedata$Working.Duration.Trial2.2 <- watermazedata$Duration.Spatial2.2
watermazedata$Working.Duration.Diff.2 <- watermazedata$Duration.Spatial2.1 - watermazedata$Duration.Spatial2.2

watermazedata$Working.Duration.Trial1.3 <- watermazedata$Duration.Spatial3.1
watermazedata$Working.Duration.Trial2.3 <- watermazedata$Duration.Spatial3.2
watermazedata$Working.Duration.Diff.3 <- watermazedata$Duration.Spatial3.1 - watermazedata$Duration.Spatial3.2

watermazedata$Working.Duration.Trial1.Ave <- (watermazedata$Duration.Spatial1.1 + watermazedata$Duration.Spatial2.1 + watermazedata$Duration.Spatial3.1) / 3
watermazedata$Working.Duration.Trial2.Ave <- (watermazedata$Duration.Spatial1.2 + watermazedata$Duration.Spatial2.2 + watermazedata$Duration.Spatial3.2) / 3
watermazedata$Working.Duration.Diff.Ave <- (watermazedata$Working.Duration.Diff.1 + watermazedata$Working.Duration.Diff.2 + watermazedata$Working.Duration.Diff.3) / 3


watermazedata$Working.Distance.Trial1.1 <- watermazedata$Distance.Spatial1.1
watermazedata$Working.Distance.Trial2.1 <- watermazedata$Distance.Spatial1.2
watermazedata$Working.Distance.Diff.1 <- watermazedata$Distance.Spatial1.1 - watermazedata$Distance.Spatial1.2

watermazedata$Working.Distance.Trial1.2 <- watermazedata$Distance.Spatial2.1
watermazedata$Working.Distance.Trial2.2 <- watermazedata$Distance.Spatial2.2
watermazedata$Working.Distance.Diff.2 <- watermazedata$Distance.Spatial2.1 - watermazedata$Distance.Spatial2.2

watermazedata$Working.Distance.Trial1.3 <- watermazedata$Distance.Spatial3.1
watermazedata$Working.Distance.Trial2.3 <- watermazedata$Distance.Spatial3.2
watermazedata$Working.Distance.Diff.3 <- watermazedata$Distance.Spatial3.1 - watermazedata$Distance.Spatial3.2

watermazedata$Working.Distance.Trial1.Ave <- (watermazedata$Distance.Spatial1.1 + watermazedata$Distance.Spatial2.1 + watermazedata$Distance.Spatial3.1) / 3
watermazedata$Working.Distance.Trial2.Ave <- (watermazedata$Distance.Spatial1.2 + watermazedata$Distance.Spatial2.2 + watermazedata$Distance.Spatial3.2) / 3
watermazedata$Working.Distance.Diff.Ave <- (watermazedata$Working.Distance.Diff.1 + watermazedata$Working.Distance.Diff.2 + watermazedata$Working.Distance.Diff.3) / 3

```

Create a "subset" dataframe for each group to ease making histograms/normal curves and QQ plots by group.

```{r}
Ac_watermazedata<-subset(watermazedata, watermazedata$Treatment=="Ac")
Fx_watermazedata<-subset(watermazedata, watermazedata$Treatment=="Fx")
Sh_watermazedata<-subset(watermazedata, watermazedata$Treatment=="Sh")
```

How many subjects are missing data from a specific column? (na = 'missing'). Make a variable that returns 1 (TRUE) if data is missing:
watermazedata$Duration.Spatial1.Missing <- is.na(watermazedata$Duration.Spatial1)

How many subjects are missing from Duration.Spatial1 data?
sum(watermazedata$Duration.Spatial1.Missing)

1 = missing, 0 = there, so mean will tell us proportion of cases missing data in that variable

....or simply calculate this WITHOUT making a new variable:

```{r}
sum(is.na(watermazedata$Duration.Cued)); mean(is.na(watermazedata$Duration.Cued))
sum(is.na(watermazedata$Duration.Spatial1)); mean(is.na(watermazedata$Duration.Spatial1))
sum(is.na(watermazedata$Duration.Spatial2)); mean(is.na(watermazedata$Duration.Spatial2))
sum(is.na(watermazedata$Duration.Spatial3)); mean(is.na(watermazedata$Duration.Spatial3))
sum(is.na(watermazedata$Duration.Spatial)); mean(is.na(watermazedata$Duration.Spatial))

sum(is.na(watermazedata$Distance.Cued)); mean(is.na(watermazedata$Distance.Cued))
sum(is.na(watermazedata$Distance.Spatial1)); mean(is.na(watermazedata$Distance.Spatial1))
sum(is.na(watermazedata$Distance.Spatial2)); mean(is.na(watermazedata$Distance.Spatial2))
sum(is.na(watermazedata$Distance.Spatial3)); mean(is.na(watermazedata$Distance.Spatial3))
sum(is.na(watermazedata$Distance.Spatial)); mean(is.na(watermazedata$Distance.Spatial))

sum(is.na(watermazedata$Speed)); mean(is.na(watermazedata$Speed))

sum(is.na(watermazedata$Probe.Entries.1)); mean(is.na(watermazedata$Probe.Entries.1))
sum(is.na(watermazedata$Probe.Entries.2)); mean(is.na(watermazedata$Probe.Entries.2))
sum(is.na(watermazedata$Probe.Entries.3)); mean(is.na(watermazedata$Probe.Entries.3))
sum(is.na(watermazedata$Probe.Entries.Ave)); mean(is.na(watermazedata$Probe.Entries.Ave))
sum(is.na(watermazedata$Probe.Percent1)); mean(is.na(watermazedata$Probe.Percent1))
sum(is.na(watermazedata$Probe.Percent2)); mean(is.na(watermazedata$Probe.Percent2))
sum(is.na(watermazedata$Probe.Percent3)); mean(is.na(watermazedata$Probe.Percent3))
sum(is.na(watermazedata$Probe.Percent.Ave)); mean(is.na(watermazedata$Probe.Percent.Ave))
sum(is.na(watermazedata$Probe2.Opposite.Percent)); mean(is.na(watermazedata$Probe2.Opposite.Percent))

sum(is.na(watermazedata$Working.Duration.Trial1.Ave)); mean(is.na(watermazedata$Working.Duration.Trial1.Ave))
sum(is.na(watermazedata$Working.Duration.Trial2.Ave)); mean(is.na(watermazedata$Working.Duration.Trial2.Ave))
sum(is.na(watermazedata$Working.Duration.Diff.Ave)); mean(is.na(watermazedata$Working.Duration.Diff.Ave))

sum(is.na(watermazedata$Working.Distance.Trial1.Ave)); mean(is.na(watermazedata$Working.Distance.Trial1.Ave))
sum(is.na(watermazedata$Working.Distance.Trial2.Ave)); mean(is.na(watermazedata$Working.Distance.Trial2.Ave))
sum(is.na(watermazedata$Working.Distance.Diff.Ave)); mean(is.na(watermazedata$Working.Distance.Diff.Ave))
```

Now, start checking the various aussumptions (normality, homogeneity of variance, etc.) for all *meaningful DVS* - e.g., Average Distance and Distance for days 1-3 are probably important, but Blocks 1-5 from each are *probably* not (?). If the idea is to compare groups, the assumptions need to be tested with each variable broken down by group.

??? is there a "Bonferroni correction" for multiple tests of Normality etc ???

Generate some descriptive stats for the variables of interest.

NOTE: For output reported using "e": e+02, simply "move" the decimal point 2 places to right. e-02 = move decimal 2 places to left...

Can use describe() (from the psych package) or stat.desc() function (from the pastecs package) to get some basic stats.

```{r}
# describe()
# Overall DVs (not broken down by group)
# single variables: by(data = dataFrame$Variable, INDICES = dataFrame$grouping DV, FUN = function)
# by(data = watermazedata$Duration.Spatial, INDICES = watermazedata$Treatment, FUN = describe)
# or
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, describe)
# multiple variables at once:
# describe(cbind(watermazedata$Duration.Cued,
#                watermazedata$Duration.Spatial1,
#                watermazedata$Duration.Spatial2,
#                watermazedata$Duration.Spatial3,
#                watermazedata$Duration.Spatial,
#                watermazedata$Distance.Cued,
#                watermazedata$Distance.Spatial1,
#                watermazedata$Distance.Spatial2,
#                watermazedata$Distance.Spatial3,
#                watermazedata$Distance.Spatial,
#                watermazedata$Speed,
#                watermazedata$Probe.Entries.1,
#                watermazedata$Probe.Entries.2,
#                watermazedata$Probe.Entries.3,
#                watermazedata$Probe.Entries.Ave,
#                watermazedata$Probe.Percent1,
#                watermazedata$Probe.Percent2,
#                watermazedata$Probe.Percent3,
#                watermazedata$Probe.Percent.Ave,
#                watermazedata$Probe2.Opposite.Percent,
#                watermazedata$Working.Duration.Trial1.Ave,
#                watermazedata$Working.Duration.Trial2.Ave,
#                watermazedata$Working.Duration.Diff.Ave,
#                watermazedata$Working.Distance.Trial1.Ave,
#                watermazedata$Working.Distance.Trial2.Ave,
#                watermazedata$Working.Distance.Diff.Ave))
# or
# describe(watermazedata[,c("Duration.Spatial1",
#                          "Duration.Spatial2",
#                          "Duration.Spatial3")]); # ETC

# broken down by group
#by(cbind(Duration.Cued=watermazedata$Duration.Cued,
#         Duration.Spatial1=watermazedata$Duration.Spatial1,
#         Duration.Spatial2=watermazedata$Duration.Spatial2,
#         Duration.Spatial3=watermazedata$Duration.Spatial3,
#         Duration.Spatial=watermazedata$Duration.Spatial,
#         Distance.Cued=watermazedata$Distance.Cued,
#         Distance.Spatial1=watermazedata$Distance.Spatial1,
#         Distance.Spatial2=watermazedata$Distance.Spatial2,
#         Distance.Spatial3=watermazedata$Distance.Spatial3,
#         Distance.Spatial=watermazedata$Distance.Spatial,
#         Speed=watermazedata$Speed,
#         Probe.Entries.1=watermazedata$Probe.Entries.1,
#         Probe.Entries.2=watermazedata$Probe.Entries.2,
#         Probe.Entries.3=watermazedata$Probe.Entries.3,
#         Probe.Entries.Ave=watermazedata$Probe.Entries.Ave,
#         Probe.Percent1=watermazedata$Probe.Percent1,
#         Probe.Percent2=watermazedata$Probe.Percent2,
#         Probe.Percent3=watermazedata$Probe.Percent3,
#         Probe.Percent.Ave=watermazedata$Probe.Percent.Ave,
#         Probe2.Opposite.Percent=watermazedata$Probe2.Opposite.Percent,
#         Working.Duration.Trial1.Ave=watermazedata$Working.Duration.Trial1.Ave,
#         Working.Duration.Trial2.Ave=watermazedata$Working.Duration.Trial2.Ave,
#         Working.Duration.Diff.Ave=watermazedata$Working.Duration.Diff.Ave,
#         Working.Distance.Trial1.Ave=watermazedata$Working.Distance.Trial1.Ave,
#         Working.Distance.Trial2.Ave=watermazedata$Working.Distance.Trial2.Ave,
#         Working.Distance.Diff.Ave=watermazedata$Working.Distance.Diff.Ave),
#   watermazedata$Treatment, describe)

# normality of overall variables
# shapiro.test(watermazedata$Duration.Cued)
# shapiro.test(watermazedata$Duration.Spatial1)
# shapiro.test(watermazedata$Duration.Spatial2)
# shapiro.test(watermazedata$Duration.Spatial3)
# shapiro.test(watermazedata$Duration.Spatial)
# shapiro.test(watermazedata$Distance.Cued)
# shapiro.test(watermazedata$Distance.Spatial1)
# shapiro.test(watermazedata$Distance.Spatial2)
# shapiro.test(watermazedata$Distance.Spatial3)
# shapiro.test(watermazedata$Distance.Spatial)
# shapiro.test(watermazedata$Speed)
# shapiro.test(watermazedata$Probe.Entries.1)
# shapiro.test(watermazedata$Probe.Entries.2)
# shapiro.test(watermazedata$Probe.Entries.3)
# shapiro.test(watermazedata$Probe.Entries.Ave)
# shapiro.test(watermazedata$Probe.Percent1)
# shapiro.test(watermazedata$Probe.Percent2)
# shapiro.test(watermazedata$Probe.Percent3)
# shapiro.test(watermazedata$Probe.Percent.Ave)
# shapiro.test(watermazedata$Probe2.Opposite.Percent)
# shapiro.test(watermazedata$Working.Duration.Trial1.Ave)
# shapiro.test(watermazedata$Working.Duration.Trial2.Ave)
# shapiro.test(watermazedata$Working.Duration.Diff.Ave)
# shapiro.test(watermazedata$Working.Distance.Trial1.Ave)
# shapiro.test(watermazedata$Working.Distance.Trial2.Ave)
# shapiro.test(watermazedata$Working.Distance.Diff.Ave)

# normality of variables broken down by group
# by(watermazedata$Duration.Cued, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Cued, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Distance.Spatial, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Speed, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Entries.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent1, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent2, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent3, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe.Percent.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Probe2.Opposite.Percent, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Duration.Trial1.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Duration.Trial2.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Duration.Diff.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Distance.Trial1.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Distance.Trial2.Ave, watermazedata$Treatment, shapiro.test)
# by(watermazedata$Working.Distance.Diff.Ave, watermazedata$Treatment, shapiro.test)
```

Using stat.desc

```{r}

# stat.desc()
# using basic = FALSE adds Shapiro-Wilks test, negating the need to run that separately as with describe()
# Overall DVs (not broken down by group)
stat.desc(cbind(Duration.Cued=watermazedata$Duration.Cued,
                Duration.Spatial1=watermazedata$Duration.Spatial1,
                Duration.Spatial2=watermazedata$Duration.Spatial2,
                Duration.Spatial3=watermazedata$Duration.Spatial3,
                Duration.Spatial=watermazedata$Duration.Spatial,
                Distance.Cued=watermazedata$Distance.Cued,
                Distance.Spatial1=watermazedata$Distance.Spatial1,
                Distance.Spatial2=watermazedata$Distance.Spatial2,
                Distance.Spatial3=watermazedata$Distance.Spatial3,
                Distance.Spatial=watermazedata$Distance.Spatial,
                Speed=watermazedata$Speed,
                Probe.Entries.1=watermazedata$Probe.Entries.1,
                Probe.Entries.2=watermazedata$Probe.Entries.2,
                Probe.Entries.3=watermazedata$Probe.Entries.3,
                Probe.Entries.Ave=watermazedata$Probe.Entries.Ave,
                Probe.Percent1=watermazedata$Probe.Percent1,
                Probe.Percent2=watermazedata$Probe.Percent2,
                Probe.Percent3=watermazedata$Probe.Percent3,
                Probe.Percent.Ave=watermazedata$Probe.Percent.Ave,
                Probe2.Opposite.Percent=watermazedata$Probe2.Opposite.Percent,
                Working.Duration.Trial1.Ave=watermazedata$Working.Duration.Trial1.Ave,
                Working.Duration.Trial2.Ave=watermazedata$Working.Duration.Trial2.Ave,
                Working.Duration.Diff.Ave=watermazedata$Working.Duration.Diff.Ave,
                Working.Distance.Trial1.Ave=watermazedata$Working.Distance.Trial1.Ave,
                Working.Distance.Trial2.Ave=watermazedata$Working.Distance.Trial2.Ave,
                Working.Distance.Diff.Ave=watermazedata$Working.Distance.Diff.Ave),
          basic = FALSE, norm = TRUE)
# or
# stat.desc(watermazedata[, c("Duration.Spatial1",
#                            "Duration.Spatial2",
#                            "Duration.Spatial3")], basic = FALSE, norm = TRUE); # ETC

# Broken down by group
# single variables: by(data = dataFrame$Variable, INDICES = dataFrame$grouping DV, FUN = function)
# by(data = watermazedata$Duration.Spatial, INDICES = watermazedata$Treatment, FUN = stat.desc)
# or
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, stat.desc)
# or
# by(watermazedata$Duration.Spatial, watermazedata$Treatment, stat.desc, basic = FALSE, norm = TRUE)
# multiple variables at once:
by(cbind(Duration.Cued=watermazedata$Duration.Cued,
         Duration.Spatial1=watermazedata$Duration.Spatial1,
         Duration.Spatial2=watermazedata$Duration.Spatial2,
         Duration.Spatial3=watermazedata$Duration.Spatial3,
         Duration.Spatial=watermazedata$Duration.Spatial,
         Distance.Cued=watermazedata$Distance.Cued,
         Distance.Spatial1=watermazedata$Distance.Spatial1,
         Distance.Spatial2=watermazedata$Distance.Spatial2,
         Distance.Spatial3=watermazedata$Distance.Spatial3,
         Distance.Spatial=watermazedata$Distance.Spatial,
         Speed=watermazedata$Speed,
         Probe.Entries.1=watermazedata$Probe.Entries.1,
         Probe.Entries.2=watermazedata$Probe.Entries.2,
         Probe.Entries.3=watermazedata$Probe.Entries.3,
         Probe.Entries.Ave=watermazedata$Probe.Entries.Ave,
         Probe.Percent1=watermazedata$Probe.Percent1,
         Probe.Percent2=watermazedata$Probe.Percent2,
         Probe.Percent3=watermazedata$Probe.Percent3,
         Probe.Percent.Ave=watermazedata$Probe.Percent.Ave,
         Probe2.Opposite.Percent=watermazedata$Probe2.Opposite.Percent),
   watermazedata$Treatment, stat.desc, basic = FALSE, norm = TRUE)
# or
# by(watermazedata[, c("Duration.Spatial1",
#                     "Duration.Spatial1",
#                     "Duration.Spatial3")],; #ETC
#   watermazedata$Treatment, stat.desc, basic = FALSE, norm = TRUE)

```

Test for homogeneity of variance among groups using the leveneTest() function from the car package (default uses median)... to use mean instead of median - for example:
leveneTest(watermazedata$Duration.Spatial, watermazedata$Treatment, center = mean)

```{r}

leveneTest(watermazedata$Duration.Cued, watermazedata$Treatment)
leveneTest(watermazedata$Duration.Spatial1, watermazedata$Treatment)
leveneTest(watermazedata$Duration.Spatial2, watermazedata$Treatment)
leveneTest(watermazedata$Duration.Spatial3, watermazedata$Treatment)
leveneTest(watermazedata$Duration.Spatial, watermazedata$Treatment)
leveneTest(watermazedata$Distance.Cued, watermazedata$Treatment)
leveneTest(watermazedata$Distance.Spatial1, watermazedata$Treatment)
leveneTest(watermazedata$Distance.Spatial2, watermazedata$Treatment)
leveneTest(watermazedata$Distance.Spatial3, watermazedata$Treatment)
leveneTest(watermazedata$Distance.Spatial, watermazedata$Treatment)
leveneTest(watermazedata$Speed, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Entries.1, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Entries.2, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Entries.3, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Entries.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Percent1, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Percent2, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Percent3, watermazedata$Treatment)
leveneTest(watermazedata$Probe.Percent.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Probe2.Opposite.Percent, watermazedata$Treatment)
leveneTest(watermazedata$Working.Duration.Trial1.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Working.Duration.Trial2.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Working.Duration.Diff.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Working.Distance.Trial1.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Working.Distance.Trial2.Ave, watermazedata$Treatment)
leveneTest(watermazedata$Working.Distance.Diff.Ave, watermazedata$Treatment)

# *** IF 1 OF 3 IS SIGNIFICANT [NOT NORMAL], DOES THAT REQUIRE NONPARAMETRIC? ***
```

Visually check each variable's data for normality / outliers averaged across all groups. This info is probably not all that interestng until broken down by group.
- Histograms w/ overlaid normal curves
- Quantile–quantile (QQ) plots
- Boxplots
- Scatterplots
- Violin plots

```{r}
# Histograms with overlaid normal curve and Quantile–quantile plots
# Scatterplots:
# p <- ggplot(watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
# use geom_crossbar()
# p + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
# Use geom_errorbar()
# p + stat_summary(fun.data=mean_sdl, fun.args = list(mult=1), geom="errorbar", color="red", width=0.2) + stat_summary(fun.y=mean, geom="point", color="red")
# Use geom_pointrange()
# p + stat_summary(fun.data=mean_sdl, fun.args = list(mult=1), geom="pointrange", color="red")

# Duration

hist.Duration.Cued <- ggplot(watermazedata, aes(Duration.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Cued, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Cued <- qplot(sample = watermazedata$Duration.Cued)
qqplot.Duration.Cued
boxplot(watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial1 <- ggplot(watermazedata, aes(Duration.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Spatial1, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial1 <- qplot(sample = watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1
boxplot(watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial2 <- ggplot(watermazedata, aes(Duration.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Spatial2, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial2 <- qplot(sample = watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2
boxplot(watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial3 <- ggplot(watermazedata, aes(Duration.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Spatial3, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial3 <- qplot(sample = watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3
boxplot(watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial <- ggplot(watermazedata, aes(Duration.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Duration.Spatial, na.rm = TRUE),
                  sd = sd(watermazedata$Duration.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial <- qplot(sample = watermazedata$Duration.Spatial)
qqplot.Duration.Spatial
boxplot(watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Distance

hist.Distance.Cued <- ggplot(watermazedata, aes(Distance.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Cued, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Cued <- qplot(sample = watermazedata$Distance.Cued)
qqplot.Distance.Cued
boxplot(watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial1 <- ggplot(watermazedata, aes(Distance.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Spatial1, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial1 <- qplot(sample = watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1
boxplot(watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial2 <- ggplot(watermazedata, aes(Distance.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Spatial2, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial2 <- qplot(sample = watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2
boxplot(watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial3 <- ggplot(watermazedata, aes(Distance.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Spatial3, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial3 <- qplot(sample = watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3
boxplot(watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial <- ggplot(watermazedata, aes(Distance.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Distance.Spatial, na.rm = TRUE),
                  sd = sd(watermazedata$Distance.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial <- qplot(sample = watermazedata$Distance.Spatial)
qqplot.Distance.Spatial
boxplot(watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Speed

hist.Speed <- ggplot(watermazedata, aes(Speed)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Speed", y = "Number")
hist.Speed +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Speed, na.rm = TRUE),
                  sd = sd(watermazedata$Speed, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Speed <- qplot(sample = watermazedata$Speed)
qqplot.Speed
boxplot(watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")
ggplot(watermazedata, aes(x=0, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Probe stuff

hist.Probe.Entries.1 <- ggplot(watermazedata, aes(Probe.Entries.1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Entries.1, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Entries.1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.1 <- qplot(sample = watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1
boxplot(watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.2 <- ggplot(watermazedata, aes(Probe.Entries.2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Entries.2, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Entries.2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.2 <- qplot(sample = watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2
boxplot(watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.3 <- ggplot(watermazedata, aes(Probe.Entries.3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Entries.3, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Entries.3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.3 <- qplot(sample = watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3
boxplot(watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Entries.Ave <- ggplot(watermazedata, aes(Probe.Entries.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Entries.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.Ave <- qplot(sample = watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave
boxplot(watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent1 <- ggplot(watermazedata, aes(Probe.Percent1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Percent1, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Percent1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent1 <- qplot(sample = watermazedata$Probe.Percent1)
qqplot.Probe.Percent1
boxplot(watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent2 <- ggplot(watermazedata, aes(Probe.Percent2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Percent2, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Percent2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent2 <- qplot(sample = watermazedata$Probe.Percent2)
qqplot.Probe.Percent2
boxplot(watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent3 <- ggplot(watermazedata, aes(Probe.Percent3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Percent3, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Percent3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent3 <- qplot(sample = watermazedata$Probe.Percent3)
qqplot.Probe.Percent3
boxplot(watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Probe.Percent.Ave <- ggplot(watermazedata, aes(Probe.Percent.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe.Percent.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent.Ave <- qplot(sample = watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave
boxplot(watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe2.Opposite.Percent <- ggplot(watermazedata, aes(Probe2.Opposite.Percent)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
                  sd = sd(watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe2.Opposite.Percent <- qplot(sample = watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent
boxplot(watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(watermazedata, aes(x=0, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Working memory stuff

hist.Working.Duration.Trial1.Ave <- ggplot(watermazedata, aes(Working.Duration.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial1.Ave <- qplot(sample = watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave
boxplot(watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Duration.Trial2.Ave <- ggplot(watermazedata, aes(Working.Duration.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial2.Ave <- qplot(sample = watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave
boxplot(watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Diff.Ave <- ggplot(watermazedata, aes(Working.Duration.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Diff.Ave <- qplot(sample = watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave
boxplot(watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Trial1.Ave <- ggplot(watermazedata, aes(Working.Distance.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial1.Ave <- qplot(sample = watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave
boxplot(watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Trial2.Ave <- ggplot(watermazedata, aes(Working.Distance.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial2.Ave <- qplot(sample = watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave
boxplot(watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Working.Distance.Diff.Ave <- ggplot(watermazedata, aes(Working.Distance.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
                  sd = sd(watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Diff.Ave <- qplot(sample = watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave
boxplot(watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=0, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=0, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

```

Now, visually check each variable data for normality / outliers broken down by group - histograms and QQ plots and box plots for each Treatment group.

```{r}
# Broken down by group (use the "subset" dataframes that were derived earlier)

# Ac
# Duration

hist.Duration.Cued <- ggplot(Ac_watermazedata, aes(Duration.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Cued, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Cued <- qplot(sample = Ac_watermazedata$Duration.Cued)
qqplot.Duration.Cued
boxplot(Ac_watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial1 <- ggplot(Ac_watermazedata, aes(Duration.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Spatial1, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial1 <- qplot(sample = Ac_watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1
boxplot(Ac_watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial2 <- ggplot(Ac_watermazedata, aes(Duration.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Spatial2, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial2 <- qplot(sample = Ac_watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2
boxplot(Ac_watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial3 <- ggplot(Ac_watermazedata, aes(Duration.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Spatial3, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial3 <- qplot(sample = Ac_watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3
boxplot(Ac_watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial <- ggplot(Ac_watermazedata, aes(Duration.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Duration.Spatial, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Duration.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial <- qplot(sample = Ac_watermazedata$Duration.Spatial)
qqplot.Duration.Spatial
boxplot(Ac_watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Distance

hist.Distance.Cued <- ggplot(Ac_watermazedata, aes(Distance.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Cued, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Cued <- qplot(sample = Ac_watermazedata$Distance.Cued)
qqplot.Distance.Cued
boxplot(Ac_watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial1 <- ggplot(Ac_watermazedata, aes(Distance.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Spatial1, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial1 <- qplot(sample = Ac_watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1
boxplot(Ac_watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial2 <- ggplot(Ac_watermazedata, aes(Distance.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Spatial2, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial2 <- qplot(sample = Ac_watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2
boxplot(Ac_watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial3 <- ggplot(Ac_watermazedata, aes(Distance.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Spatial3, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial3 <- qplot(sample = Ac_watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3
boxplot(Ac_watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial <- ggplot(Ac_watermazedata, aes(Distance.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Distance.Spatial, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Distance.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial <- qplot(sample = Ac_watermazedata$Distance.Spatial)
qqplot.Distance.Spatial
boxplot(Ac_watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Speed

hist.Speed <- ggplot(Ac_watermazedata, aes(Speed)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Speed", y = "Number")
hist.Speed +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Speed, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Speed, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Speed <- qplot(sample = Ac_watermazedata$Speed)
qqplot.Speed
boxplot(Ac_watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Probe stuff

hist.Probe.Entries.1 <- ggplot(Ac_watermazedata, aes(Probe.Entries.1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Entries.1, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Entries.1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.1 <- qplot(sample = Ac_watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1
boxplot(Ac_watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.2 <- ggplot(Ac_watermazedata, aes(Probe.Entries.2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Entries.2, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Entries.2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.2 <- qplot(sample = Ac_watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2
boxplot(Ac_watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.3 <- ggplot(Ac_watermazedata, aes(Probe.Entries.3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Entries.3, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Entries.3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.3 <- qplot(sample = Ac_watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3
boxplot(Ac_watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.Ave <- ggplot(Ac_watermazedata, aes(Probe.Entries.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Entries.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.Ave <- qplot(sample = Ac_watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave
boxplot(Ac_watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent1 <- ggplot(Ac_watermazedata, aes(Probe.Percent1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Percent1, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Percent1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent1 <- qplot(sample = Ac_watermazedata$Probe.Percent1)
qqplot.Probe.Percent1
boxplot(Ac_watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent2 <- ggplot(Ac_watermazedata, aes(Probe.Percent2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Percent2, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Percent2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent2 <- qplot(sample = Ac_watermazedata$Probe.Percent2)
qqplot.Probe.Percent2
boxplot(Ac_watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent3 <- ggplot(Ac_watermazedata, aes(Probe.Percent3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Percent3, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Percent3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent3 <- qplot(sample = Ac_watermazedata$Probe.Percent3)
qqplot.Probe.Percent3
boxplot(Ac_watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent.Ave <- ggplot(Ac_watermazedata, aes(Probe.Percent.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe.Percent.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent.Ave <- qplot(sample = Ac_watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave
boxplot(Ac_watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe2.Opposite.Percent <- ggplot(Ac_watermazedata, aes(Probe2.Opposite.Percent)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe2.Opposite.Percent <- qplot(sample = Ac_watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent
boxplot(Ac_watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Working memory stuff

hist.Working.Duration.Trial1.Ave <- ggplot(Ac_watermazedata, aes(Working.Duration.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial1.Ave <- qplot(sample = Ac_watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave
boxplot(Ac_watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Trial2.Ave <- ggplot(Ac_watermazedata, aes(Working.Duration.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial2.Ave <- qplot(sample = Ac_watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave
boxplot(Ac_watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Diff.Ave <- ggplot(Ac_watermazedata, aes(Working.Duration.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Diff.Ave <- qplot(sample = Ac_watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave
boxplot(Ac_watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Trial1.Ave <- ggplot(Ac_watermazedata, aes(Working.Distance.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial1.Ave <- qplot(sample = Ac_watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave
boxplot(Ac_watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Trial2.Ave <- ggplot(Ac_watermazedata, aes(Working.Distance.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial2.Ave <- qplot(sample = Ac_watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave
boxplot(Ac_watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Diff.Ave <- ggplot(Ac_watermazedata, aes(Working.Distance.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Ac_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
                  sd = sd(Ac_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Diff.Ave <- qplot(sample = Ac_watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave
boxplot(Ac_watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Ac_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Fx
# Duration

hist.Duration.Cued <- ggplot(Fx_watermazedata, aes(Duration.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Cued, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Cued <- qplot(sample = Fx_watermazedata$Duration.Cued)
qqplot.Duration.Cued
boxplot(Fx_watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial1 <- ggplot(Fx_watermazedata, aes(Duration.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Spatial1, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial1 <- qplot(sample = Fx_watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1
boxplot(Fx_watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial2 <- ggplot(Fx_watermazedata, aes(Duration.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Spatial2, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial2 <- qplot(sample = Fx_watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2
boxplot(Fx_watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial3 <- ggplot(Fx_watermazedata, aes(Duration.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Spatial3, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial3 <- qplot(sample = Fx_watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3
boxplot(Fx_watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial <- ggplot(Fx_watermazedata, aes(Duration.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Duration.Spatial, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Duration.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial <- qplot(sample = Fx_watermazedata$Duration.Spatial)
qqplot.Duration.Spatial
boxplot(Fx_watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Distance

hist.Distance.Cued <- ggplot(Fx_watermazedata, aes(Distance.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Cued, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Cued <- qplot(sample = Fx_watermazedata$Distance.Cued)
qqplot.Distance.Cued
boxplot(Fx_watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial1 <- ggplot(Fx_watermazedata, aes(Distance.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Spatial1, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial1 <- qplot(sample = Fx_watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1
boxplot(Fx_watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial2 <- ggplot(Fx_watermazedata, aes(Distance.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Spatial2, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial2 <- qplot(sample = Fx_watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2
boxplot(Fx_watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial3 <- ggplot(Fx_watermazedata, aes(Distance.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Spatial3, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial3 <- qplot(sample = Fx_watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3
boxplot(Fx_watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial <- ggplot(Fx_watermazedata, aes(Distance.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Distance.Spatial, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Distance.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial <- qplot(sample = Fx_watermazedata$Distance.Spatial)
qqplot.Distance.Spatial
boxplot(Fx_watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Speed

hist.Speed <- ggplot(Fx_watermazedata, aes(Speed)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Speed", y = "Number")
hist.Speed +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Speed, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Speed, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Speed <- qplot(sample = Fx_watermazedata$Speed)
qqplot.Speed
boxplot(Fx_watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Probe stuff

hist.Probe.Entries.1 <- ggplot(Fx_watermazedata, aes(Probe.Entries.1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Entries.1, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Entries.1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.1 <- qplot(sample = Fx_watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1
boxplot(Fx_watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.2 <- ggplot(Fx_watermazedata, aes(Probe.Entries.2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Entries.2, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Entries.2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.2 <- qplot(sample = Fx_watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2
boxplot(Fx_watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.3 <- ggplot(Fx_watermazedata, aes(Probe.Entries.3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Entries.3, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Entries.3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.3 <- qplot(sample = Fx_watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3
boxplot(Fx_watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.Ave <- ggplot(Fx_watermazedata, aes(Probe.Entries.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Entries.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.Ave <- qplot(sample = Fx_watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave
boxplot(Fx_watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent1 <- ggplot(Fx_watermazedata, aes(Probe.Percent1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Percent1, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Percent1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent1 <- qplot(sample = Fx_watermazedata$Probe.Percent1)
qqplot.Probe.Percent1
boxplot(Fx_watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent2 <- ggplot(Fx_watermazedata, aes(Probe.Percent2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Percent2, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Percent2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent2 <- qplot(sample = Fx_watermazedata$Probe.Percent2)
qqplot.Probe.Percent2
boxplot(Fx_watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent3 <- ggplot(Fx_watermazedata, aes(Probe.Percent3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Percent3, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Percent3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent3 <- qplot(sample = Fx_watermazedata$Probe.Percent3)
qqplot.Probe.Percent3
boxplot(Fx_watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent.Ave <- ggplot(Fx_watermazedata, aes(Probe.Percent.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe.Percent.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent.Ave <- qplot(sample = Fx_watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave
boxplot(Fx_watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe2.Opposite.Percent <- ggplot(Fx_watermazedata, aes(Probe2.Opposite.Percent)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe2.Opposite.Percent <- qplot(sample = Fx_watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent
boxplot(Fx_watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Working memory stuff

hist.Working.Duration.Trial1.Ave <- ggplot(Fx_watermazedata, aes(Working.Duration.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial1.Ave <- qplot(sample = Fx_watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave
boxplot(Fx_watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Trial2.Ave <- ggplot(Fx_watermazedata, aes(Working.Duration.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial2.Ave <- qplot(sample = Fx_watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave
boxplot(Fx_watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Diff.Ave <- ggplot(Fx_watermazedata, aes(Working.Duration.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Diff.Ave <- qplot(sample = Fx_watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave
boxplot(Fx_watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Trial1.Ave <- ggplot(Fx_watermazedata, aes(Working.Distance.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial1.Ave <- qplot(sample = Fx_watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave
boxplot(Fx_watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Trial2.Ave <- ggplot(Fx_watermazedata, aes(Working.Distance.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial2.Ave <- qplot(sample = Fx_watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave
boxplot(Fx_watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Diff.Ave <- ggplot(Fx_watermazedata, aes(Working.Distance.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Fx_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
                  sd = sd(Fx_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Diff.Ave <- qplot(sample = Fx_watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave
boxplot(Fx_watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Fx_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Sh
# Duration

hist.Duration.Cued <- ggplot(Sh_watermazedata, aes(Duration.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Duration", y = "Number")
hist.Duration.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Cued, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Cued <- qplot(sample = Sh_watermazedata$Duration.Cued)
qqplot.Duration.Cued
boxplot(Sh_watermazedata$Duration.Cued, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial1 <- ggplot(Sh_watermazedata, aes(Duration.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Spatial1, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial1 <- qplot(sample = Sh_watermazedata$Duration.Spatial1)
qqplot.Duration.Spatial1
boxplot(Sh_watermazedata$Duration.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


hist.Duration.Spatial2 <- ggplot(Sh_watermazedata, aes(Duration.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Spatial2, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial2 <- qplot(sample = Sh_watermazedata$Duration.Spatial2)
qqplot.Duration.Spatial2
boxplot(Sh_watermazedata$Duration.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial3 <- ggplot(Sh_watermazedata, aes(Duration.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Spatial3, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial3 <- qplot(sample = Sh_watermazedata$Duration.Spatial3)
qqplot.Duration.Spatial3
boxplot(Sh_watermazedata$Duration.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Duration.Spatial <- ggplot(Sh_watermazedata, aes(Duration.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Duration", y = "Number")
hist.Duration.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Duration.Spatial, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Duration.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Duration.Spatial <- qplot(sample = Sh_watermazedata$Duration.Spatial)
qqplot.Duration.Spatial
boxplot(Sh_watermazedata$Duration.Spatial, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Distance

hist.Distance.Cued <- ggplot(Sh_watermazedata, aes(Distance.Cued)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Cued Distance", y = "Number")
hist.Distance.Cued +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Cued, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Cued, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Cued <- qplot(sample = Sh_watermazedata$Distance.Cued)
qqplot.Distance.Cued
boxplot(Sh_watermazedata$Distance.Cued, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial1 <- ggplot(Sh_watermazedata, aes(Distance.Spatial1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Spatial1, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Spatial1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial1 <- qplot(sample = Sh_watermazedata$Distance.Spatial1)
qqplot.Distance.Spatial1
boxplot(Sh_watermazedata$Distance.Spatial1, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial2 <- ggplot(Sh_watermazedata, aes(Distance.Spatial2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Spatial2, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Spatial2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial2 <- qplot(sample = Sh_watermazedata$Distance.Spatial2)
qqplot.Distance.Spatial2
boxplot(Sh_watermazedata$Distance.Spatial2, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial3 <- ggplot(Sh_watermazedata, aes(Distance.Spatial3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Spatial3, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Spatial3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial3 <- qplot(sample = Sh_watermazedata$Distance.Spatial3)
qqplot.Distance.Spatial3
boxplot(Sh_watermazedata$Distance.Spatial3, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Distance.Spatial <- ggplot(Sh_watermazedata, aes(Distance.Spatial)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Spatial Distance", y = "Number")
hist.Distance.Spatial +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Distance.Spatial, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Distance.Spatial, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Distance.Spatial <- qplot(sample = Sh_watermazedata$Distance.Spatial)
qqplot.Distance.Spatial
boxplot(Sh_watermazedata$Distance.Spatial, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Speed

hist.Speed <- ggplot(Sh_watermazedata, aes(Speed)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Speed", y = "Number")
hist.Speed +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Speed, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Speed, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Speed <- qplot(sample = Sh_watermazedata$Speed)
qqplot.Speed
boxplot(Sh_watermazedata$Speed, main="Boxplots by Group", xlab="Group", ylab="Speed")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")


# Probe stuff

hist.Probe.Entries.1 <- ggplot(Sh_watermazedata, aes(Probe.Entries.1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Average Entries", y = "Number")
hist.Probe.Entries.1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Entries.1, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Entries.1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.1 <- qplot(sample = Sh_watermazedata$Probe.Entries.1)
qqplot.Probe.Entries.1
boxplot(Sh_watermazedata$Probe.Entries.1, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.2 <- ggplot(Sh_watermazedata, aes(Probe.Entries.2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Entries.2, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Entries.2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.2 <- qplot(sample = Sh_watermazedata$Probe.Entries.2)
qqplot.Probe.Entries.2
boxplot(Sh_watermazedata$Probe.Entries.2, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.3 <- ggplot(Sh_watermazedata, aes(Probe.Entries.3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Entries.3, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Entries.3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.3 <- qplot(sample = Sh_watermazedata$Probe.Entries.3)
qqplot.Probe.Entries.3
boxplot(Sh_watermazedata$Probe.Entries.3, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Entries.Ave <- ggplot(Sh_watermazedata, aes(Probe.Entries.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Entries", y = "Number")
hist.Probe.Entries.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Entries.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Entries.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Entries.Ave <- qplot(sample = Sh_watermazedata$Probe.Entries.Ave)
qqplot.Probe.Entries.Ave
boxplot(Sh_watermazedata$Probe.Entries.Ave, main="Boxplots by Group", xlab="Group", ylab="Entries")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent1 <- ggplot(Sh_watermazedata, aes(Probe.Percent1)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent1 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Percent1, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Percent1, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent1 <- qplot(sample = Sh_watermazedata$Probe.Percent1)
qqplot.Probe.Percent1
boxplot(Sh_watermazedata$Probe.Percent1, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent2 <- ggplot(Sh_watermazedata, aes(Probe.Percent2)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent2 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Percent2, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Percent2, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent2 <- qplot(sample = Sh_watermazedata$Probe.Percent2)
qqplot.Probe.Percent2
boxplot(Sh_watermazedata$Probe.Percent2, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent3 <- ggplot(Sh_watermazedata, aes(Probe.Percent3)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent3 +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Percent3, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Percent3, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent3 <- qplot(sample = Sh_watermazedata$Probe.Percent3)
qqplot.Probe.Percent3
boxplot(Sh_watermazedata$Probe.Percent3, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe.Percent.Ave <- ggplot(Sh_watermazedata, aes(Probe.Percent.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe.Percent.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe.Percent.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe.Percent.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe.Percent.Ave <- qplot(sample = Sh_watermazedata$Probe.Percent.Ave)
qqplot.Probe.Percent.Ave
boxplot(Sh_watermazedata$Probe.Percent.Ave, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Probe2.Opposite.Percent <- ggplot(Sh_watermazedata, aes(Probe2.Opposite.Percent)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Percent", y = "Number")
hist.Probe2.Opposite.Percent +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Probe2.Opposite.Percent, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Probe2.Opposite.Percent <- qplot(sample = Sh_watermazedata$Probe2.Opposite.Percent)
qqplot.Probe2.Opposite.Percent
boxplot(Sh_watermazedata$Probe2.Opposite.Percent, main="Boxplots by Group", xlab="Group", ylab="Percent")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Working memory stuff

hist.Working.Duration.Trial1.Ave <- ggplot(Sh_watermazedata, aes(Working.Duration.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Duration.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial1.Ave <- qplot(sample = Sh_watermazedata$Working.Duration.Trial1.Ave)
qqplot.Working.Duration.Trial1.Ave
boxplot(Sh_watermazedata$Working.Duration.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Trial2.Ave <- ggplot(Sh_watermazedata, aes(Working.Duration.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Duration.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Trial2.Ave <- qplot(sample = Sh_watermazedata$Working.Duration.Trial2.Ave)
qqplot.Working.Duration.Trial2.Ave
boxplot(Sh_watermazedata$Working.Duration.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Duration.Diff.Ave <- ggplot(Sh_watermazedata, aes(Working.Duration.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Duration", y = "Number")
hist.Working.Duration.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Duration.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Duration.Diff.Ave <- qplot(sample = Sh_watermazedata$Working.Duration.Diff.Ave)
qqplot.Working.Duration.Diff.Ave
boxplot(Sh_watermazedata$Working.Duration.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Duration")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Trial1.Ave <- ggplot(Sh_watermazedata, aes(Working.Distance.Trial1.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial1.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Distance.Trial1.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial1.Ave <- qplot(sample = Sh_watermazedata$Working.Distance.Trial1.Ave)
qqplot.Working.Distance.Trial1.Ave
boxplot(Sh_watermazedata$Working.Distance.Trial1.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Trial2.Ave <- ggplot(Sh_watermazedata, aes(Working.Distance.Trial2.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Trial2.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Distance.Trial2.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Trial2.Ave <- qplot(sample = Sh_watermazedata$Working.Distance.Trial2.Ave)
qqplot.Working.Distance.Trial2.Ave
boxplot(Sh_watermazedata$Working.Distance.Trial2.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

hist.Working.Distance.Diff.Ave <- ggplot(Sh_watermazedata, aes(Working.Distance.Diff.Ave)) + 
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  labs(x = "Distance", y = "Number")
hist.Working.Distance.Diff.Ave +
  stat_function(fun = dnorm, args = list
                (mean = mean(Sh_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE),
                  sd = sd(Sh_watermazedata$Working.Distance.Diff.Ave, na.rm = TRUE)),
                colour = "black", size = 1)
qqplot.Working.Distance.Diff.Ave <- qplot(sample = Sh_watermazedata$Working.Distance.Diff.Ave)
qqplot.Working.Distance.Diff.Ave
boxplot(Sh_watermazedata$Working.Distance.Diff.Ave, main="Boxplots by Group", xlab="Group", ylab="Distance")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(Sh_watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

```

Now visually compare groups against each other

```{r}
# Broken down by group all on 1 graph

# Duration

ggplot(watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Duration.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Distance

ggplot(watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Cued)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Cued, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Distance.Spatial, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Speed

ggplot(watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Speed)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Speed, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Probe stuff

ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Entries.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent1)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent1, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent2)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent2, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent3)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent3, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe.Percent.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Probe2.Opposite.Percent, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

# Working memory stuff

ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Duration.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial1.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Trial2.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")

ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_boxplot() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  geom_jitter(color="black", size=0.4, alpha=0.9) +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("A boxplot with jitter") +
  xlab("")
scttr <- ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave)) + geom_dotplot(binaxis='y', stackdir='center')
scttr + stat_summary(fun.data="mean_sdl", fun.args = list(mult=1), geom="crossbar", width=0.5, color="red")
ggplot(watermazedata, aes(x=Treatment, y=Working.Distance.Diff.Ave, fill="white")) +
  geom_violin() +
  scale_fill_viridis(discrete = TRUE, alpha=0.6, option="A") +
  theme_ipsum() +
  theme(
    legend.position="none",
    plot.title = element_text(size=11)
  ) +
  ggtitle("Violin chart") +
  xlab("")
```


Meeting assumptions of normality / homogeneity of variance can be tough w/ large data sets because small variations can be "significant" (you can also test homogeneity of variance w/ "variance ratio" or Hartley's Fmax). Either way, if data are not normally distributed and of equal variances, parametric tests are not valid. To correct "problems" with the data:

Outliers
- remove the case / subject (especially if it was somehow "different")
- "bring the case it into the fold" using the mean + 2 or 3 SDs - Change the score to be the mean + 2 or 3 SDs
- "bring the case into the fold" using the next highest score plus one method - Change the score to be one unit above the next highest score in the data set

For non-normally-distributed data:
- Can also use "trimmed means" (removing a specific % of cases have been removed from each end)
- Can also use "M-estimator" which empirically derives the proper % to trim
- Can also use bootstrapping to estimate "true" mean / variance
- Transform the data: log, square root, or reciprocal transformations can correct for positive skew and/or unequal variance. If data are negatively skewed, you need derive a reciprocal score (reverse the scores by subtracting each score from the highest score obtained)
-- Make new transformed DVs using newVariable <- function(oldVariable)
--- Square root: watermazedata$Duration.Spatial.Sqrt <- sqrt(watermazedata$Duration.Spatial)
--- Absolute value: watermazedata$Duration.Spatial.Abs <- abs(watermazedata$Duration.Spatial.Diff)
--- Log (natural): watermazedata$Duration.Spatial.Log <- log(watermazedata$Duration.Spatial +1)
+1 needed to avoid trying to calculate log of 0
--- Log (base 10): watermazedata$Duration.Spatial.Log10 <- log10(watermazedata$Duration.Spatial)
+1 needed for base 10???
--- Reciprocal: watermazedata$Duration.Spatial.Reciprocal <- 1/(watermazedata$Duration.Spatial +1)  +1 needed to avoid trying to divide by zero